Natural catastrophes and man-made disasters in 2016 · 2019-12-12 · Economic losses from natural catastrophes and man-made disasters across the world were an estimated USD 175 billion

No 2 /2017

Natural catastrophes and man-made disasters in 2016: a year of widespread damages

01 Executive summary02 Catastrophes in 2016:

global overview06 Regional overview13 Floods in the US – an

underinsured risk18 Tables for reporting

year 201640 Terms and selection

criteria

Swiss Re sigma No 2/2017 1

Executive summary

In terms of devastation wreaked, there were a number of large-scale disasters across the world in 2016, including earthquakes in Japan, Ecuador, Tanzania, Italy and New Zealand. There were also a number of severe floods in the US and across Europe and Asia, and a record high number of weather events in the US. The strongest was Hurricane Matthew, which became the first Category 5 storm to form over the North Atlantic since 2007, and which caused the largest loss of life – more than 700 victims, mostly in Haiti – of a single event in the year. Another expansive, and expensive, disaster was the wildfire that spread through Alberta and Saskatchewan in Canada from May to July.

In total, in sigma criteria terms, there were 327 disaster events in 2016, of which 191 were natural catastrophes and 136 were man-made. Globally, approximately 11 000 people lost their lives or went missing in disasters. At USD 175 billion, total economic losses1 from disasters in 2016 were the highest since 2012, and a significant increase from USD 94 billion in 2015. As in the previous four years, Asia was hardest hit. The earthquake that hit Japan’s Kyushu Island inflicted the heaviest economic losses, estimated to be between USD 25 billion and USD 30 billion.

Global insured losses from catastrophes were also the highest since 2012, at around USD 54 billion in 2016, up from USD 38 billion in 2015. The implication of the increase is that many tens of thousands of policyholders in disaster events benefitted from having insurance cover in place, to receive speedy indemnification for their property losses, get their businesses back up and running quickly, and mitigate other economic and humanitarian hardships. For example, the wildfires in Canada devastated many homes and around 88 000 people were evacuated. In response, once the evacuation order was lifted, insurance personnel were given access to the affected regions to provide immediate assistance to returning residents. The outcome was that 68% of all personal property claims had been settled by the end of the year.2 Another example was Hurricane Matthew, where a USD 23.4 million payout from the Caribbean Catastrophe Risk Insurance Facility to Haiti meant that thousands of displaced persons received food and shelter, and the authorities were able to buy medications.3 A testimony to the positive impact of public/private partnership in insurance.

However, insurance cover is not universal. There was an all-peril global catastrophe protection gap of USD 121 billion in 2016. So while a high-level of insurance penetration in New Zealand meant that households and business were well equipped to recover from the damage caused by the quake that struck the South Island in October 2016, less than 20% (USD 4.9 billion) of the economic losses resulting from the earthquake in Kyushu Island were covered by insurance. And in Ecuador, the quake on the same day as the one in Japan caused estimated economic losses of USD 4 billion and insured losses of just USD 0.5 billion, a coverage schism of USD 3.5 billion, or 88%.

2016 was also a year of many severe precipitation events globally which in turn triggered major flooding over large areas. The US experienced multiple severe floods throughout the year, with Louisiana worst hit. In China there was extensive flooding along the Yangtze River basin in July. In view of the year’s many damaging floods, this sigma assesses the flood protection gap in the US. Increased wealth and larger populations have elevated society’s exposure to flood risk everywhere in the world, including the US. Today the majority of US flood coverage comes from the National Flood Insurance Program (NFIP), but the flood protection gap of around USD 10 billion annually shows that even the US remains critically under-insured for flood risk.

1 From hereon, “total economic losses” expressed as “economic losses”.2 Value taken from CatIQ data set.3 Government of Haiti helps 1.4 million persons affected by Hurricane Matthew with CCRIF’s Payouts,

CCRIF SPC, 7 November 2016, http://www.ccrif.org/news/government-haiti-helps-14-million-persons-affected-hurricane-matthew-ccrif-payouts

There were a number of expansive disaster events in 2016 …

… leading to the highest level of overall losses since 2012.

Insured losses from catastrophes were USD 54 billion, meaning many thousands caught in a disaster were better able to recover from the losses and hardships inflicted.

Nevertheless, the global catastrophe protection gap remains substantial.

This sigma includes a feature on underinsurance of flood risk in the US.

2 Swiss Re sigma No 2/2017

Catastrophes in 2016: global overview

Number of events: 327

In sigma criteria terms, there were 327 catastrophes worldwide in 2016, down from 356 in 2015. There were 191 were natural catastrophes compared with 199 in 2015, and 136 man-made disasters (down from 157).

Source: Cat Perils and Swiss Re Institute.

To classify as a catastrophe according to sigma criteria, an event must lead to economic losses, insured claims or casualties in excess of the thresholds detailed in Table 1.

Insured losses (claims)

Maritime disasters USD 19.9 million

Aviation USD 39.8 millionOther losses USD 49.5 million

or Total economic losses USD 99.0 million

or CasualtiesDead or missing 20Injured 50Homeless 2000


Number of victims: approximately 11 000

Approximately 11 000 people lost their lives or went missing in natural and man-made disasters in 2016. That was lower than in 2015 and one of the lowest recorded in a single year. There were approximately 7000 victims in natural catastrophes. Hurricane Matthew in Haiti and the earthquake that struck Ecuador in April claimed most lives, and a number of people also died in heat waves and floods in other countries.

There were 191 natural and 136 man-made disasters in 2016.

Figure 1 Number of catastrophic events, 1970–2016

Natural catastrophes

Man-made disasters

Man-made disasters Natural catastrophes

0

50

100

150

200

250

300

2016201020052000199519901985198019751970

The sigma event selection criteria.

Table 1 The sigma event selection criteria for 2016

Approximately 7000 people died or went missing in natural catastrophes …


There were roughly 4000 deaths in man-made disasters in 2016, compared with around 7000 in 2015. A boat carrying migrants sank off the coast of Crete on 3 June 2016, an accident in which 358 perished. The total number of reported deaths in maritime disasters fell to 1596 from 2487 in 2015, but many more are believed to have died in unreported incidents of boats carrying migrants sinking.

Other man-made events taking many lives included the collapse of a church roof in Nigeria, killing 160 people. In aviation disasters, 384 people died compared with 685 in 2015, with most of the fatalities in two plane crashes. In November, a jet travelling to Medellín in Colombia crashed after running out of fuel, killing 71. And on Christmas Day, an aircraft crashed shortly after take-off from Adler in Russia, killing 92. There were also 766 deaths in major fire and explosion events in 2016.

Note: Scale is logarithmic: the number of victims increases tenfold per band.


Total economic losses: USD 175 billion

Economic losses from natural catastrophes and man-made disasters across the world were an estimated USD 175 billion in 2016. This was almost double than in 2015 (USD 94 billion), and in line with the inflation-adjusted average of USD 175 billion of the previous 10 years. Catastrophe losses in 2016 were 0.24% of global gross domestic product (GDP), again in line with the 10-year average.

Natural catastrophe-related economic losses were around USD 166 billion in 2016, coming mostly from earthquakes, tropical cyclones, other severe storms and droughts in Asia, North America and Europe. Man-made disasters are estimated to have caused USD 9 billion of the economic losses, down from USD 12 billion in 2015.

… and around 4000 perished in man-made disasters.

Airplane crashes and other man-made disasters claimed many victims.

Figure 2 Number of victims, 1970–2016

1 1970: Bangladesh storm2 1976: Tangshan earthquake, China3 1991: Cyclone Gorky, Bangladesh4 2004: Indian Ocean earthquake

and tsunami5 2008: Cyclone Nargis, Myanmar6 2010: Haiti earthquake7 2013: Typhoon Haiyan, Philippines8 2015: Earthquake in Nepal

1 2

34 5

6

7 8

Man-made disasters Natural catastrophes

1000

10 000

100 000

1 000 000

10 000 000

1970 1975 1980 1985 1990 1995 2000 2005 2010 2016

Economic losses in 2016 in line with the 10-year average.

Global natural catastrophe-related losses were around USD 166 billion.


Catastrophes in 2016: global overview

Regions USD bn* % of GDP

North America 59 0.29%Latin America & Caribbean 6 0.14%Europe 16 0.08%Africa 3 0.14%Asia 83 0.32%Oceania/Australia 6 0.45%Seas / space 1Total 175World total 0.24%10-year average ** 175 0.24%

* rounded** inflation adjustedSource: Swiss Re Institute.

Insured losses: USD 54 billion

The insurance industry covered close to USD 54 billion – less than one third – of the economic losses from natural and man-made disasters in 2016, up from USD 38 billion in 2015 and in line with the inflation-adjusted annual average of the previous 10 years (USD 53 billion). Natural catastrophes resulted in claims of USD 46 billion, the same as the previous 10-year annual average. Insured losses from man-made disasters were USD 8 billion, down from USD 10 billion in 2015.

The natural catastrophe-associated insured losses were 0.06% of world GDP in 2016 and 2.9% of global property direct premiums written (DPW), in line with the respective 10-year annual averages. Overall insured losses from natural catastrophes and man-made disasters were 0.07% of GDP and 3.4% of DPW.


Table 2 Economic losses in USD billion and as a % of global GDP, 2016

Insured losses from natural hazards and man-made disasters were in line with the 10-year annual average …

… and equivalent to 0.07% of GDP.

Figure 3 Insured catastrophe losses 1970–2016 in USD billion, at 2016 prices

1 1992: Hurricane Andrew 2 1994: Northridge earthquake 3 1999: Winter Storm Lothar 4 2001: 9/11 attacks 5 2004: Hurricanes Ivan, Charley, Frances 6 2005: Hurricanes Katrina, Rita, Wilma 7 2008: Hurricanes Ike, Gustav 8 2010: Chile, New Zealand earthquakes 9 2011: Japan, New Zealand earthquakes,

Thailand flood10 2012: Hurricane Sandy

Man-made disasters

Weather-related catastrophes

Earthquake/tsunami

23 4

5

6

7

2016201020052000199519901985198019751970

0

20

40

60

80

100

120

140

Earthquake/tsunami Man-made disastersWeather-related catastrophes

1 8

9

10


The largest insurance loss event globally in 2016 was the earthquake in Japan in April, which triggered claims of USD 4.9 billion. The second costliest was Hurricane Matthew in the US and the Caribbean, which resulted in insured losses of USD 4 billion. Twelve disasters triggered insured claims of USD 1 billion or more in 2016 (see Table 7), up from six such events in 2015.

Figure 4 shows the difference between insured and economic losses over time, termed the insurance protection gap. It is the amount of financial loss generated by catastrophes not covered by insurance. In 2016, the global protection gap was approximately USD 121 billion. The rate of growth of economic losses has outpaced the growth of insured losses over the last 25 years. In terms of 10-year rolling averages, insured losses grew by 4.6% between 1991 and 2016, and economic losses by 5.6%.

Economic losses = insured + uninsured losses


The largest single insurance-loss event of 2016 was the earthquake in Japan in April.

The global insurance protection gap was USD 121 billion in 2016.

Figure 4 Insured vs uninsured losses 1970–2016 in USD billion, at 2016 prices

Uninsured losses

Insured losses

Insured losses

Uninsured losses

10-year moving average (total insured losses)

10-year moving average (total economic lossses)

2016201020052000199519901985198019751970

0

50

100

150

200

250

300

350

400

450


Regional overview

Earthquakes, tropical cyclones and other storms in many parts of the world caused the highest insured losses in 2016. In Asia, the earthquake in Japan in April led to the biggest losses in the region, in both economic and insured loss terms. In the US, Hurricane Matthew and flooding in Louisiana caused the largest economic losses.

Insured losses Economic losses

Region Number Victims in % in USD bn in % in USD bn in %

North America 66 1005 9.2% 30.4 56.6% 59.5 34.1%Latin America & Caribbean 22 1009 9.3% 1.4 2.5% 6.4 3.7%Europe 51 1509 13.8% 7.5 14.0% 15.5 8.9%Africa 44 1761 16.2% 1.7 3.2% 3.0 1.7%Asia 128 5309 48.7% 8.8 16.4% 83.0 47.6%Oceania/Australia 7 52 0.5% 3.4 6.4% 6.4 3.6%Seas / space 9 253 2.3% 0.5 0.9% 0.8 0.4%World* 327 10 898 100.0% 54 100.0% 175 100.0%

*Includes some rounded totals.


North America

In North America, insured losses from disaster events were USD 30 billion in 2016, the highest of all regions. Most of the losses came from hurricanes, hailstorms, thunderstorms and severe flood events in the US. In Canada, wildfires from May to July caused the highest insured losses ever recorded there.

The 2016 North Atlantic hurricane season produced 15 named storms (11 in 2015), seven of which became hurricanes (four in 2015) and three were “major” hurricanes (Category 3 or stronger on the Saffir-Simpson scale). Hurricane Hermine in early September was the first to make landfall in Florida since Wilma in 2005, coming in at Category 1. Later that same month, Hurricane Matthew, the strongest of the season, became the first Category 5 storm to form over the North Atlantic since Hurricane Felix in 2007. Hurricane Matthew hit Haiti as Category 4 but by the time it made US landfall in South Carolina, it had weakened to Category 1. Last year continued the decade-long stretch of no “major” hurricanes making US landfall, the longest since the 1860s.

Hurricane Matthew and resulting storm surge caused wind and flood damage, beach erosion and infrastructure damage in Florida through North Carolina. Long after moving in from the eastern seaboard, moisture from record sea surface temperatures and associated storms brought downpours and inland flooding in the Carolinas, Georgia and Virginia, leading to heavy damage to agriculture. Economic losses from Matthew in the US and the Caribbean were approximately USD 12 billion, of which about USD 4 billion were insured. It could have been worse if, at Category 4, the centre of the storm had not stayed offshore. But if the US was spared the worst, the Caribbean was not. The Category 4 winds that hit Haiti caused devastation and took many lives there, and also in Cuba and the Bahamas.

In mid-August, moisture from the Gulf of Mexico brought record precipitation over the Amite and Comite rivers basins, triggering major flooding particularly in the region of Baton Rouge, the capital of the State of Louisiana. More than 30 000 people had to be rescued from floodwaters and, at the height of the flood, 100 000 people were displaced. Sadly, 13 people died. As water receded, 50 000 houses, 20 000 vehicles and 20 000 businesses were left damaged or destroyed, leading to estimated economic losses of USD 10 billion. The insured losses, however, were USD 3.1 billion, evidence of a large flood protection gap.

By region, insured losses were highest in North America in 2016.

Table 3 Number of events, victims, economic and insured losses by region, 2016

Severe weather, floods and wildfires caused most losses.

The number of storms in the North Atlantic hurricane season was above the long-term average.

Hurricane Matthew brought wind and flood damage to southeast US.

Moisture from the Gulf of Mexico triggered pluvial flooding in Louisiana.

https://www.ncdc.noaa.gov/monitoring-content/sotc/national/2016/ann/Hermine_160907_cotton.pdf


According to a preliminary count from the Storm Prediction Centre of the National Oceanic and Atmospheric Administration (NOAA), there were 1060 tornadoes in the US in 2016, below the annual average of 1221 of the Doppler radar era. Nevertheless, insured losses from tornado outbreaks and thunderstorms (severe convective storms) reached an estimated USD 15 billion, higher than in 2015 (USD 9.7 billion) and also higher than the previous 10-year annual average of USD 12.6 billion. In the spring, two severe hailstorms in Texas led to combined insured losses of about USD 4.7 billion. There were four independent severe convective storms in the US that caused losses of USD 1 billion or more, compared to just one in 2015. And there were 33 thunderstorms in 2016, a record high.

Other parts of the US and North America experienced severe dry weather conditions, and there were several wildfires. The most destructive in terms of buildings destroyed and number of hectares burnt was the Fort McMurray fire in Alberta, Canada. The resulting insured losses were close to USD 2.8 billion4, making it the biggest insurance loss event ever in Canada, and the second costliest wildfire on sigma records, globally.

Scientists expect an increase in both the frequency and the severity of wildfires as a result of climate change,5 with warmer and drier climates providing favourable conditions for burning. For example, the length of wildfire season has extended by 2.5 months over the last 30 years, according to the World Resources Institute.6 Modest changes to precipitation rates and temperature can greatly influence conditions for large fires. An estimated 2°C mean temperature increase could extend the annual area burned in wildfires by 1.4 to 5 times in western US states, according to scientists publishing in Conservation Biology.7 These large fires are also costly. In 2015, the US Forest Service spent more than half its annual budget combating forest fires. In 1995, fighting fires took up 16% of the budget.8

Canada burning: growing exposure yields large wildfire lossesThe Fort McMurray wildfire spread through Alberta and Saskatchewan from May to July 2016. The exact cause of the fire is unknown, but the authorities suspect it was due to human activity. Once ignited, high temperatures, low humidity and strong gusting winds contributed to the rapid spread of the fire. In addition, below-average precipitation rates in the preceding autumn and low snowfall in the winter associated with El Niño had dried out the vegetation, providing ample fuel for the flames to grow. The fire was declared contained on 5 July, having damaged approximately 2400 structures in Fort McMurray and burnt 590 000 hectares of forest land. During the course of the fire, 88 000 residents were evacuated from impacted areas.

Economic losses from the Fort McMurray fire were an estimated USD 3.95 billion.9 Statistics Canada estimates that 7.6 million net work hours were lost due to the fire in the Fort McMurray area, and the rest of Alberta experienced a loss of 2.9 million work hours.10 The overall financial impact, including indirect losses such as lost work hours, could be as high as USD 7 billion (CAD 9.5 billion).11 During the fire, crude

4 Data from CatIQ.5 Chmura et al., “Forest responses to climate change in the northwestern United States: Ecophysiological

foundations for adaptive management”, Forest Ecology and Management, 2011.6 Western U.S. Wildfires and the Climate Change Connection, World Resources Institute, September

2014, http://climatechange.lta.org/wildfires/7 McKenzie et al., “Climate change, wildfire and conservation”, Conservation Biology, vol. 18, issue 4,

2004.8 The Rising Cost of Fire Operations: Effects on the Forest Service’s Non-fire Work, United States

Department of Agriculture, 4 August 2015, https://www.fs.fed.us/sites/default/files/2015-Fire-Budget-Report.pdf

9 Economic Impacts of the 2016 Alberta Wildfires, The Conference Board of Canada, 17 May 2016, http://www.conferenceboard.ca/press/newsrelease/16-05-17/economic_impacts_of_the_fort_mcmurray_wildfires.aspx

10 Wildfires in northern Alberta: Impact on hours worked, May and June, 2016, Statistics Canada, 25 November 2016 http://www.statcan.gc.ca/daily-quotidien/161125/dq161125a-eng.htm

11 “Financial impact of Fort McMurray wildfire reaches $9.5 billion: study”, Canadian Underwriter, 17 January 2017, http://www.canadianunderwriter.ca/catastrophes/financial-impact-fort-mcmurray-wildfire-reaches-9-5-billion-study-1004107558/

Tornado activity was below average, while insured losses from severe convective storms were above usual.

The costliest fire event in North America in 2016 was the Fort McMurray wildfire in Canada.

Warmer and drier climate will create favourable conditions for wildfires.

Climate-influenced conditions allowed for rapid spread of the fire.

The Fort McMurray wildfire resulted in Canada's largest insured loss ever.


Regional overview

bitumen and synthetic crude oil production was reduced by 47 million barrels. The Conference Board of Canada estimates USD 1 billion in lost revenue. From the insurance perspective, the fire was the costliest event ever in Canada. The insured losses were USD 2.8 billion, nearly double the previous highest insurance event in the country, the flooding in Alberta in 2013. The associated high insurance penetration rates in the area, the proximity of the fire to the city of Fort McMurray, and the devastation of many surrounding neighbourhoods led to the record loss.

Canada has the third largest oil reserves in the world, nearly all of which are in Alberta’s oil sands.12 The production capacity of the area has increased substantially, from about 1 million to more than 2 million barrels a day over the last decade. Alongside the build-up of production assets, the population of the Fort McMurray area – the Regional Municipality of Wood Buffalo – has grown rapidly to a pre-fire population of nearly 72 000 permanent residents, according to Statistics Canada. This in turn has pushed up property prices and the overall value of asset exposure. The municipality’s economic report from late 2014 said the average home price in the areas was USD 459 000, well above average prices in cities such as Calgary or Edmonton, and the average household income is one of the highest in the whole country.

Wildfires are an-ever present hazard in the forest and grassland regions of Canada and North America, and are an essential part of the forest ecosystem. Figure 5 shows the insured losses from wildfires in the US, Canada and Australia, which together account for the great majority of fire-related losses globally. Most fires do not threaten communities, but some destroy vast expanses of timber resources. Insured losses from wildfires have been growing since 1980, and this is likely to continue as exposures in wildfire-prone regions continue to increase given expanding populations, the building of more property and infrastructure, and the possible effects of changing climates such as warmer and drier seasons.

Numbers above the bars denote the number of wildfire events.


The Fort McMurray fire is the second costliest wildfire on sigma records. Only the 1991 Oakland Hills, California, fire resulted in higher insured losses (USD 3 billion).

12 Data from Natural Resources Canada, http://www.nrcan.gc.ca/energy/oil-sands/18085

The expansion of oil sand operations and subsequent increased exposures contributed to the large losses, …

… and the exposures are only likely to grow further.

Figure 5 Insured losses from wildfires in the US, Canada and Australia 1980–2016 in USD billion, at 2016 prices

0

1

2

3

4

5

6

7

8

9

Losses

2010–20162000–20091990–19991980–1989

20

15

6

4

The McMurray fire is the second costliest wildfire on record.



Europe

In Europe, economic losses from natural and man-made disasters were USD 15.5 billion in 2016, of which USD 7.5 billion were covered by the insurance industry. Most of the losses came from earthquakes in Italy, and thunderstorms and pluvial floods in central Europe.

On 24 August 2016, a magnitude 6.2 earthquake hit the Apennines Mountains in central Italy, killing 299 people and devastating the small towns of Amatrice, Accumoli and Pescara del Tronto. The event was just the first of an extended series of damaging quakes in the region. On 26 October 2016, two aftershocks of magnitude 5.5 and 6.1 hit Visso, north of Amatrice, and on 30 October, a magnitude 6.6 quake struck Norcia, which lies between Amatrice and Visso. That last seism was the most powerful to hit Italy since 1980 and was felt through most of the country. The October shocks did not claim any lives thanks to the widespread evacuation of the area after the August quake, but they did add to the damage and destruction of buildings already weakened by the earlier earthquake event, and displaced thousands of residents. The combined economic losses from all the quakes were USD 6 billion, only a fraction of which were insured. The area is mainly rural, mountainous and scarcely populated, but the shallow depth of the tremors and the unreinforced buildings magnified the impact of the quakes.

Italy’s first seismic building code dates to 1909, but seismic mapping of the whole country only came into effect in 2003. Italy has a long history of damaging earthquakes. In 1908, Messina in Sicily was hit by a magnitude 7.2 earthquake and tsunami that claimed about 86 000 victims, making it the deadliest earthquake documented in Europe. A few years later, in 1915 a magnitude 7.0 quake struck the same area as the 2016 shakes, killing more than 30 000 people. And as Table 4 shows, six of the top 10 costliest earthquakes in Europe since 1970 have been in Italy.

Figure 6 Costliest wildfires events 1980–2016 in USD billion, at 2016 prices

Insured losses

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1991US

2016CAN

2007US

2003US

2003US

1993US

2009AUS

2015US

2016US

2008US

3.02.81.51.31.21.21.10.90.90.7InsuredLoss

Earthquakes and flooding caused the heaviest losses in Europe.

Central Italy was repeatedly hit by earthquakes in August and October.

The country has a long history of damaging quakes.


Regional overview

Year Country Location Economic Losses

1 1980 Italy Irpinia 34.42 1999 Turkey Izmit 28.83 1976 Italy Friuli 14.64 2012 Italy Emilia Romagna (2 events) 17.35 1977 Romania Vrancea 6.76 2016 Italy Central Italy (2 events) 67 1999 Greece Athens 4.98 1979 Montenegro Ulcinj 4.69 2009 Italy L’Aquila 4

10 1997 Italy Umbria 3


Europe also suffered heavy storms and subsequent flooding events in 2016. At the end of May and beginning of June, thunderstorms, torrential rain and flooding – river and flash floods – hit France, southern and central Germany and Belgium, leading to combined insured losses of USD 2.9 billion. According to the Deutscher Wetterdienst (German Weather Service), the flash floods were the worst ever seen in Germany.

Once again, terrorists targeted Europe in 2016. The deadliest attack was in Nice during Bastille Day celebrations, when a lorry ploughed through a crowd of people, killing 84 people and injuring 202.

Asia

As in the previous four years, in 2016 Asia suffered higher economic losses due to natural and man-made catastrophes than any other region of the world. Economic losses from disaster events in Asia were an estimated USD 83 billion in 2016, of which approximately USD 9 billion were covered by insurance. The most destructive event was the magnitude 7.0 earthquake that hit Kyushu Island in southern Japan, close to the city of Kumamoto on 16 April 2016. It was the main quake of a series of notable fore- and aftershocks that stretched from 14 April to 19 April. A total of 137 people died and close to 2000 people were injured. The earthquake triggered landslides that complicated disaster relief efforts. More than 8500 buildings were destroyed, and an estimated 160 000 buildings were damaged. Economic losses were estimated to be between USD 25 billion and USD 30 billion, of which USD 4.9 billion were insured.

China suffered many damaging floods in 2016, the most devastating along Yangtze River basin in July. Extreme rainfall caused pluvial and river floods, and also landslides in 11 provinces, with Hubei worst hit. The spread of the floods was accelerated by many localised precipitation events which caused the Yangtze and its tributaries to overflow. Economic losses were estimated at USD 22 billion, making it the costliest Yangtze River flood event since 1998. Since the 1998 floods, there has been massive investment in flood defences, and these helped curtail the economic losses in 2016. With low insurance penetration, however, insured losses from the 2016 floods were just USD 0.4 billion.

Table 4 Costliest earthquakes in Europe since 1970 in USD billion, at 2016 prices

In 2016, parts of Europe were hit by heavy rains and widespread flooding.

There were also several terror attacks in Europe last year.

Asia has suffered the biggest losses from catastrophic events for five years running.

Severe floods hit the Yangtze River basin.


Latin America and the Caribbean

Natural catastrophes and man-made disasters caused economic losses of more than USD 6 billion in Latin America and the Caribbean in 2016. Insured losses were approximately USD 1.4 billion. The main drivers were earthquakes, hurricanes and floods.

A magnitude 7.8 earthquake struck offshore near the central coast of Ecuador on the same day (16 April 2016) as the quake that stuck Kyushu Island in Japan. In Ecuador, there were 673 victims in the earthquake, along with widespread damage in the provinces of Esmeraldas and Manabí. This was the deadliest earthquake of 2016 globally and, with estimated economic losses of USD 4 billion, the costliest natural catastrophe event in Ecuador on sigma records. Insured losses, however, were just USD 0.5 billion.

Later in the year, Hurricane Matthew made landfall in the southern provinces of Haiti on 4 October 2016 as a Category 4 storm, the first since 1964. It also made landfall in Cuba and the Bahamas, but most of the devastation was in Haiti. There 674 people lost their lives, the deadliest event to hit Haiti since the earthquake in 2010.

Oceania

Disaster events in Oceania triggered insured losses of USD 3.4 billion in 2016. The 13 November 2016 earthquake with magnitude of 7.8 on New Zealand’s South Island caused most losses. The epicentre of the quake was around 93 km north of Christchurch and caused widespread damage in Kaikoura, a small tourist town. It also ruptured a series of six faults along the northeastern coast of the South Island. This was the most damaging quake in New Zealand since the shocks in 2010 and 2011 nearer to Christchurch. Last year’s earthquake was stronger than in 2010/2011, but at USD 1.7 billion to USD 2.4 billion, the insured losses were lower because the quake struck a less heavily populated area.

The earthquake did trigger a tsunami, but the effect of the latter was dampened by coastal uplift which occurred during the shock, and also because the tsunami occurred at low tide. Geologists estimate that there were between 80 000 and 100 000 landslides in the aftermath of the quake.13 Landslides can disrupt the flow of water and create landslide dams, which can pose additional hazards if the dams break. Landslides can also leave much debris and cut businesses and communities off from their supply chains and transportation routes.

Earlier in the year, in February the Category 5 Tropical Cyclone Winston hit Fiji. Winds up to 295 km/h and a storm surge cut a path of destruction across all four divisions of Fiji, claiming 44 lives. Overall, it caused economic losses of USD 1.4 billion (31% of GDP14), including severe losses for local sugar plantations.

In Australia, a winter storm – an East Coast Low – brought damaging winds, large waves, coastal erosion, and very heavy rainfall between 4–7 June 2016, causing flooding in areas of southeast Queensland, eastern New South Wales, eastern Victoria and large areas of northern Tasmania. The estimated insured losses were USD 0.3 billion. There were also some small wildfire and severe weather events in Australia, but their overall associated losses were below average.

13 “Landslides and Landslide dams caused by the Kaikoura Earthquake”, geonet.org.nz, November 2016, http://info.geonet.org.nz/display/quake/2016/11/18/Landslides+and+Landslide+dams+caused+by+the+Kaikoura+Earthquake

14 Emergency Assistance for Recovery from Tropical Cyclone Winston, Asian Development Bank, June 2016, https://www.adb.org/sites/default/files/project-document/185540/50181-001-rrp.pdf

Insured losses in Latin America were over USD 1 billion in 2016.

A powerful earthquake hit Ecuador, the deadliest earthquake of the year.

Hurricane Matthew brought devastation to the Caribbean.

An earthquake on New Zealand s South Island was the biggest insurance loss event in Oceania.

Damage from subsequent landslides can cut supply chains.

The Category 5 Cyclone Winston caused large losses in Fiji …

… while it was a relatively quiet year for natural catastrophes in Australia.

http://www.bom.gov.au/climate/current/statements/scs57.pdf


Regional overview

Africa

Natural catastrophes and man-made disasters in Africa claimed approximately 1800 lives and caused economic losses of close to USD 3 billion in 2016. Insured losses were USD 1.7 billion, mostly relating to claims for accidents in oil and gas facilities. A magnitude 5.9 earthquake hit near the west shore of the Lake Victoria Basin between Tanzania, Uganda and Rwanda. The area is predominantly rural and 41% of the buildings are made of mud15 and are hence vulnerable to quakes, and 117 721 people lost their homes.

A year of strong earthquakes and high uninsured lossesThere were several major earthquakes in 2016, including the almost simultaneous quakes that struck Japan and Ecuador on 16 April 2016, and others in Italy and New Zealand later in the year. The combined economic loss of all seismic shocks in 2016 was an estimated USD 43 billion, of which around USD 9 billion was covered by insurance, signalling a still-large earthquake protection gap.

The protection gap is a global, not just an emerging market issue. Yes, the earthquake in Ecuador did cause economic losses of USD 4 billion and insured losses of just USD 0.5 billion. But the coverage schism is no less dramatic in many advanced markets. The quake in Japan on the same day resulted in economic losses of USD 25 billion to USD 30 billion, while insured losses were USD 4.9 billion. This was the second costliest earthquake in Japan in terms of insured claims on sigma records, primarily because of the increased uptake of residential property earthquake insurance since the Kobe earthquake of 1995. On the other hand, the level of earthquake insurance penetration for commercial property in Japan is among the lowest of the advanced countries, in spite of Japan being very prone to earthquakes.

It is a similar story in Italy, which is also earthquake-prone. The quakes that hit central Italy in August and October caused combined economic losses of USD 6 billion and insured losses of just about USD 0.2 billion, according to Perils AG. Italy is the eighth largest economy in the world, but just 1% of residential buildings are insured against quake risk. Historically, the state has intervened with ex-post disaster programmes set up under the pressure of the emergency, resulting in long-lasting and much-more-than-budgeted-for reconstruction drives. Public debate regarding the need for increased insurance penetration, or for alternative public solutions, arises in the wake of each disaster. But to date, no associated legislation has been enacted.

At the opposite end of the spectrum is New Zealand. According to the New Zealand Earthquake Commission, about 95% of buildings carry earthquake coverage,16 due to a government-based initiative to promote public- and private-sector insurance schemes. Earthquake cover is provided to those who have bought private fire (property) insurance, which most people have. That means in New Zealand, households and businesses are better equipped to cope with a major earthquake event. Hence, while the October 2016 resulted in economic losses of USD 3.9 billion, of those USD 1.7-2.4 billion were covered by some form of insurance solution.

The low frequency of major earthquakes creates a false perception among exposed populations that earthquakes are not a major risk. This, and the absence of government awareness campaigns, means that the take-up rates of insurance protection remain low. The series of earthquakes in 2016 are not the biggest seismic events to have ever occurred. However, the death and destruction caused by these quakes is a stark reminder of the vulnerability of many people around the world.

15 “Deadly earthquake in Tanzania (also felt in Uganda, Rwanda and Kenya)”, earthquake_report.com, 10 September 2016, http://earthquake-report.com/2016/09/10/strong-earthquake-lake-victoria-region-on-september-10-2016/

16 “Damage and losses to residential buildings during the Canterbury earthquake sequence”, nzsee.org.nz, 2016 http://www.nzsee.org.nz/db/2016/Papers/O-04%20Horspool.pdf

In Africa, approximately 1800 people died in disaster events in 2016.

Economic losses from all earthquakes in 2016 were USD 43 billion. The associated insured losses were just USD 9 billion.

The earthquake protection gap is as much an advanced as an emerging market problem.

For example, In Italy just 1% of residential buildings are insured for earthquake risk.

The New Zealand experience demonstrates the positive benefit that earthquake insurance offers.

Many communities are exposed to earthquakes, but have no form of associated risk protection.


Floods in the US – an underinsured risk

A long history of floods

The US has suffered many major flood events over the course of its history. Figure 7 shows the level of flood related insurance losses covered by the US National Flood Insurance Program (NFIP), the public-sector and largest insurer of residential flood risk in the US, since 1978. Last year was no exception in the history of water inundation. The worst event was in mid-August, when extreme rainfall triggered major inland flooding in southern Louisiana and Mississippi, resulting in economic losses of US 10 billion. It was the costliest flood event in the US since Hurricane Sandy in 2012, a Category 1 storm that also led to widespread flooding as a result of storm surges on the east coast.

Source: NFIP.

The Louisiana and Mississippi floods were the result of a stationary low pressure system combining with tropical moisture from the Gulf of Mexico, bringing record precipitation levels to the Amite and Comite river basins. The rains were heaviest in the south, including in and around Baton Rouge, the state capital of Louisiana. Many rivers burst their banks, flooding adjacent areas. Independent flash- and backwater flooding incidents which covered wide areas added to the havoc.

The topography of southern Louisiana makes the region particularly vulnerable to flooding. It is largely wet and low-lying land, through which many large rivers run, and it is also exposed to the moisture from the Gulf of Mexico. The state has battled with expansive flooding – whether induced by tropical storms, torrential rains or rising waters – from early times. Very memorably, in 2005 it was New Orleans, further south on the coast of Louisiana, that bore most of the damage when the catastrophic failure of the levees let in the waters from the storm surges set off by Hurricane Katrina.

The Louisiana flood was just one of several damaging flood events in the US in 2016. There were four separate multi-billion-dollar-loss inland floods (unrelated to tropical cyclones), the highest number to have occurred in a single year since 1980, according to the NOAA. Three of the floods were clustered in Louisiana and Texas between March and August, causing combined economic losses of USD 16 billion.17 And on the eastern seaboard, remnants of Hurricane Matthew caused inland flooding in North Carolina. The US was also hit by heavy floods in 2015, when inland flooding in South Carolina, Texas, Oklahoma, Missouri and the Midwest caused combined losses of more than USD 5 billion.

17 2016: A historic year for billion-dollar weather and climate disasters in US, climate.gov, 9 January 2017, https://www.climate.gov/news-features/blogs/beyond-data/2016-historic-year-billion-dollar-weather-and-climate-disasters-us

Heavy rains in Louisiana in 2016 caused the biggest flood losses in the US since Hurricane Sandy in 2012.

Figure 7 Total US insured NFIP losses by decade, 1978–2016, in USD billion

Insured losses

2008–20161998–20071988–19971978–1987

0

5

10

15

20

25

30

The precipitation was triggered by tropical moisture from the Gulf of Mexico.

Louisiana is highly exposed to flood risk from multiple natural hazards.

In 2016 there were many damaging floods in the US.



Flood types

The experience of 2016 and recent years demonstrates that the US remains highly vulnerable to flood risk, as Figure 8 also suggests. Floods can come from different sources: in coastal states from storm surges, and inland from heavy precipitation, leading to fluvial (river water) and pluvial (surface water) flooding.

Source: US National Park Service, Swiss Re CatNet®.

Storm surgesThe most severe water damage in the US is associated with storm surge-driven flooding. Storm surges are when, in a storm, seawater levels rise above tide levels to form powerful flood waves that travel inland. The most dangerous storm surges typically result from tropical cyclones along coastal areas in southeastern states and along the east coast. For instance, most of the loss of life and damage from Hurricane Katrina, the costliest recorded tropical cyclone, was inflicted by storm surges. Saltwater flooding in coastal areas can also be extreme when storm surges coincide with high tides, as was the case in Hurricane Sandy in 2012.

River floodsThere are two forms of freshwater flooding. The first is river (fluvial) flooding, which results from a combination of contributing effects. Key drivers are long durations of heavy rainfall, particularly when preceded by heavy snowfall and then rapid snowmelt, filling river basins. Antecedent conditions like saturated soils can accelerate the build-up of water. Flooding occurs when the amount of water exceeds a river’s capacity, and the surplus water overflows (breaks) the river’s banks. This type of flooding occurs all through the US, but is particularly prevalent in the Midwest due to the confluence of large rivers, heavy precipitation and snowmelt. The Mississippi basin, for example, has been the scene of repeated (documented) major flood events, in 1809, 1825, 1844, 1851, 1927, 1937, 1973, 1993, 2008 and in 2011.

Pluvial floodsThe second form of freshwater flooding is pluvial floods. These generally occur when there are large amounts of rainfall which the land surface cannot absorb or, in the case of urban flooding, which overwhelms the drainage system. The floods in Louisiana in 2016 and in South Carolina in 2015 were both pluvial floods.

The US is exposed to flood risk from storm surges, river overflows and heavy rains.

Figure 8 Swiss Re US flood zones

Storm surges cause most flood-related losses.

The Mississippi basin and Midwest have endured repeated flood events over history.

Pluvial floods are triggered by heavy downpours.


The weather systems that trigger extreme precipitation in the US include tropical moisture from the Gulf of Mexico and the Atlantic Ocean, and in the western states moisture from the Pacific. In the west, some of the most dramatic precipitation events are triggered by the so-called pineapple express, a type of atmospheric river consisting of narrow bands of moisture extending from the tropical Pacific Ocean to the coast of California. As the moisture hits the Sierra Nevada mountain range, heavy rains result. This is what happened at the end of 1996 and beginning of 1997, for example, when heavy precipitation led to widespread flooding and localised landslides in the western states of California, Oregon, Washington, Nevada and Idaho.

Extreme precipitation can also result from severe convective activity. For example, the precipitation that led to the 2016 Louisiana flood came from large thunderstorms activity fed by high levels of moisture from the Gulf of Mexico. US severe convective storms – large thunderstorms – are the most violent in the world and can wreak havoc through powerful tornadoes and large hail. But they can also trigger pluvial floods when they unleash extreme precipitation in urban drainage basins. In recent years, metropolitan areas such as Houston, Atlanta, Nashville, Oklahoma City, Dallas, Kansas City, Chicago and Detroit have all suffered severe pluvial flash floods, sometimes repeatedly. While river floods are rare, flash and pluvial floods generally occur frequently. They can happen almost anywhere and with little warning, they can last from a few hours to weeks, and can impact a wide range of spatial ranges, from single catchments or cities to entire river basins across multiple states.

The US flood protection gap

The US is vast and has a great diversity of climatic regimes, meaning there are also many flood-generating natural hazards. Meantime, population growth and urbanisation has increased the exposure potential. There have been significant investments in infrastructure to mitigate flood hazard and regulate development in flood-prone areas. Nevertheless, urbanisation continues to extend to more flood-prone areas. For example, the Houston metropolitan area has expanded rapidly in the past 15 years, with the suburban sprawl spilling onto floodplains prone to flash floods in heavy rains.18 In towns and cities there are fewer avenues for water discharge, and urbanisation also leads to more water-impermeable surfaces like roads and parking lots. The multi-billion dollar losses that Houston suffered after two separate inland flood events in 2016 and 2015 are not entirely inexplicable.

Scientists expect flood events to become more frequent as rising temperatures load the atmosphere with more vapour, which will translate into more frequent downpours. The combination of population growth, urban development and more extreme weather events as temperatures rise all point to more extreme flood events also, and an increase in the associated costs.

Yet, the US has been and continues to be critically under-insured with respect to flood risk. Table 5 lists the costliest flood events in the US since 1978 in economic loss terms, expressed in 2016 prices. The numbers estimate the economic losses from water damage only in the individual events, many of which also caused wind damage. The isolation of water-inflicted losses facilitates simple quantification of the protection gap for homeowners in the respective events. Table 5 also indicates the percentage of the losses carried by the NFIP. Using Hurricane Sandy in 2012 as an example, the economic losses from water damage were USD 70 billion, of which 17% were covered by the NFIP. Despite the NFIP, a significant portion of homeowners were uninsured and had to shoulder losses on their own.

18 “How US inland flood became a peak peril”, carriermanagement.com, 25 July 2016, http://www.carriermanagement.com/features/2016/07/25/156981.htm?bypass=c5c8e489f258184e403c97515bf8c4b2

Tropical moisture can intensify precipitation.

Thunderstorms can also cause pluvial flooding.

Population growth and urbanisation increase national exposure to flood risk.

So too does the likelihood of more frequent extreme weather events.

Even so, the US remains under-insured for flood risk.



Type of flood

Year

Event

Economic losses from flood

damage

NFIP losses as % of economic losses

from flood damage

1 Storm surge 2005 Hurricane Katrina, storm surge 140 17%2 Storm surge 2012 Hurricane Sandy, storm surge 70 17%3 Freshwater 1993 Midwest flooding 57 1%4 Storm surge 2008 Hurricane Ike, storm surge 15 22%5 Freshwater 2001 Tropical storm Allison – inland

flood15 13%

6 Freshwater 2008 Iowa and Midwest flood 13 1%7 Storm surge 2004 Hurricane Ivan, storm surge 11 22%8 Freshwater 2016 Severe storms and flooding in

Louisiana10 21%

9 Freshwater 1997 Northern Plains, Upper Midwest flood

8 4%

10 Freshwater 1996 West Coast Flood 7 1%

Note: Economic losses are adjusted for GDP growth.

Source: NFIP, Cat Perils and Swiss Re Institute.

A shortcoming of loss experience from a selection of individual events is that they do not necessarily reveal the true extent of underlying risk. The historical timescale of events in Table 5 is relatively short, and some high impact/low frequency events that may not occur for several decades may be unaccounted for in the data set. For better understanding of the flood protection gap, flood catastrophe models have been developed to provide a more complete view of both high and low frequency events by going back over a longer period of time. Models can also be used to estimate the future impact of more frequent flooding events.

According to Swiss Re’s proprietary in-house catastrophe models, economic losses from flood events in the US are expected to amount to USD 15 billion annually. Of the economic losses each year, storm surges are estimated to account for on average USD 8 billion, with inland flooding the cause of the remaining USD 7 billion. And of the economic losses, only USD 5 billion are insured, meaning an annual protection gap of around USD 10 billion. Business segments with a high insurance penetration are commercial and industrial lines, with frequent all-risk policy covers. The gap is largest for small businesses and homeowners, despite the growth in NFIP coverage following the major flood events of past years

The flood protection gap is second only to the expected annual shortfall in earthquake insurance cover (USD 20 billion). The two perils have key differences. While earthquake risk is relatively concentrated in California, flood risk is distributed throughout the US. And while earthquakes are considered a more severe peril, resulting in very high losses, floods tends to occur more frequently, with lower associated losses.

Closing the protection gapFor the annual expected USD 5 billion in insured flood-related losses in the US, the single largest insurer of residential flood risk is the NFIP, a branch of the Federal Emergency Management Agency (FEMA). The aim of this public scheme is to provide affordable insurance to homeowners and to encourage municipal authorities and communities to adopt and enforce floodplain management regulations and thus mitigate flood risk. There is also a mandatory insurance program, but that’s only for federally-backed mortgaged homes in high flood risk zones. On average, about 15% of US flood losses are borne by NFIP. Besides the NFIP, the private insurance industry does offer a few flood insurance products, but these are very niche (eg, excess NFIP covers for high net value homes) and not widely available. Ultimately, the great majority of US households remain heavily exposed to flood risk, to the tune of USD 10 billion annually. This places a heavy burden on households, society and the economy in general.

Table 5 Economic losses from US flood events in USD billion, and NFIP losses as a % of economic losses

Loss experience may not reveal the true underlying risk.

Storm surges drive most of the flood losses in the US.

The insurance shortfall for flood risk is second only to the earthquake protection gap.

The Federal Emergency Management Agency provides flood coverage to homeowners through the NFIP.


The flood protection gap can be addressed. First, households need better understanding of their exposure. Often homeowners do not grasp the full extent of their exposure to flood risk, or assume they are already covered through their standard homeowner policies. Second, homeowners need access to simple flood insurance products which are easily understood and comprehensive. Third, private/public partnerships can support financial resilience, for example by supporting covers for homeowners in high-risk zones at affordable prices.

Adverse selection is one of the key reasons for the lack of private flood insurance provision. For premiums to remain affordable and insurance to be sustainable, the risk must be spread among as many policyholders as possible. In the case of floods, reaching such critical mass is more challenging because homeowners can “select against” insurers by buying cover only in areas they consider to be at high risk of flooding. However, today risk assessment tools exist that allow insurers to fairly price flood risk by using location-specific risk based premiums, thus widening the insurability of flood risk. With the ever-changing nature of flood risk, regular updates of flood hazard maps are a necessary foundation for accurate risk assessment. So too is extending the assessment to consider forward-looking climate change studies to provide a basis for long-term sustainable planning.

Several other tools are available to insurers to increase flood risk coverage, including better understanding of behavioural patterns. When deciding to purchase flood insurance, rational decision-making factors such as affordability come into play, but so do behavioural biases like mental barriers or lack of awareness. Evidence shows that people tend to purchase flood insurance based on their experience of past events, and that they stop renewing their cover after enough time has passed since a loss occurred. Recent advances in behavioural sciences can help improve the perception of the value proposition of existing products, and create new concepts on how to offer insurance more effectively. Through a test-and-learn approach, insurance products and customer experiences can be designed in ways that align with the psychology of decision making.

Reinsurance can also play a role in closing the protection gap, and is already doing so. The two largest storm surge events of recent years – Katrina and Sandy – generated total claims of USD 24.5 billion, causing severe funding issues for the NFIP. For this reason, in 2017 FEMA purchased reinsurance to offload some of the risk to the private sector. The placement transferred USD 1.042 billion in risk above a USD 4 billion deductible to 25 reinsurance companies.19 Closing the flood protection gap in the US requires the collaboration of all stakeholders, in the private and public sectors, and is achievable. The expertise and tools needed to provide comprehensive and affordable flood insurance to most US homeowners are available today.

19 FEMA’s 2017 Reinsurance Program Better Manages Future Flood Risk, FEMA,3 January 2017, https://www.fema.gov/news-release/2017/01/03/femas-2017-reinsurance-program-better-manages-future-flood-risk

Actions for resilience: improve risk awareness and make insurance products simple.

Risk assessments tools are available to manage adverse selection and widen the insurability of flood risk.

Behavioural sciences can help improve consumers' perception of flood insurance.

Flood insurance can be available for the majority of US homeowners.


Tables for reporting year 2016

Table 6 Overall losses in 2016, by peril type

Number as % Victims as %

insured loss (in USD mn) as %

Natural catastrophes 191 58.4% 6884 63.2% 45 944 85.5%Earthquakes 16 1386 9046Floods 65 3336 5694Storms 82 1640 20 334Drought, bush fires, heat waves 16 340 4664Hail 6 0 6236Cold, frost 5 158 0Other natural catastrophes 1 24 0Man-made disasters 136 41.6% 4014 36.8% 7797 14.5%Mining accidents 8 166 184Rail disasters (incl. cableways) 11 318 87Aviation disasters 11 3.4% 384 3.5% 248 0.5%

Crashes 7 383 117Space 2 0 41

Damage on ground 2 1 90Major fires, explosions 47 14.5% 766 7.1% 4643 8.7%

Other fires, explosions 5 159 617Other buildings 11 387 0Industry, warehouses 17 84 2027Oil, gas 13 136 1921Department stores 1 0 78

Miscellaneous 20 6.1% 684 6.3% 173 0.3%

Terrorism 15 601 173Other miscellaneous losses 4 83 0Social unrest 1 0 0

Maritime disasters 36 11.1% 1596 14.7% 2463 4.6%Tankers 5 66 98Passenger ships 19 1530 0Other maritime accidents 3 0 420Drilling platforms 9 0 1944

Collapse of buildings/bridges 3 0.9% 100 0.9% 0Total 327 100.0% 10 864 100.0% 53 516 100.0%



Table 7 The 20 most costly insurance losses in 2016

Insured loss (in USD mn) Victims Date (start) Event Country/region

4887 137 14.4.2016 Earthquakes Japan4000 734 6.10.2016 Hurricane Matthew US and the Caribbean3102 13 11.8.2016 Severe storms and flooding in Louisiana US2995 – 10.4.2016 Severe hailstorm in San Antonio, TX US2886 17 27.5.2016 Storms/floods (low-pressure systems Elvira and Friederike) Germany, France2782 – 2.5.2016 Fort McMurray wildfires Canada

1700-2400

2 14.11.2016 Earthquake Mw 7.8 New Zealand

1689 – 23.3.2016 North Texas hailstorm, thunderstorms USns – 28.2.2016 Turret failure at a floating production, storage and offloading (FPSO) vessel Ghana

1187 6 29.4.2016 Thunderstorms, large hail, tornadoes, flash floods US1135 – 28.7.2016 Thunderstorms, severe hail damage in CO, hailstorm in Wyoming US1037 8 16.4.2016 Flash flood, river flood in Houston region from torrential rains US

920 1 17.3.2016 Thunderstorms, large hail in Forth Worth and Arlington in TX US919 14 28.11.2016 Chimney Tops 2 Fire spreads to forest areas in dry conditions US874 1 21.5.2016 Thunderstorms, tornadoes, hail US764 2 7.5.2016 Thunderstorms, hail, tornadoes US666 - 11.5.2016 Thunderstorms, hail, tornadoes US639 6 25.4.2016 Thunderstorms, hail, tornadoes US637 10 22.2.2016 Thunderstorms, 50 tornadoes, hail US, Canadans* - 31.3.2016 Steam generator falls and causes damage to nuclear power plant France

ns = not showingSource: Cat Perils and Swiss Re Institute.



Table 8 The 20 worst catastrophes in terms of victims in 2016

VictimsInsured loss (in USD mn) Date (start) Event Country/region

734 4000 28.9.2016 Hurricane Matthew US and the Caribbean673 500 16.4.2016 Earthquake Mw 7.8 Ecuador538 – 29.8.2016 Remnants of Typhoon Lionrock trigger floods along Tumen River North Korea358 – 3.6.2016 Boat carrying migrants capsizes Greece, Mediterranean Sea300 – 13.4.2016 Heat waves India299 69 24.8.2016 Earthquake Mw 6.2 Italy289 403 30.6.2016 Severe floods along Yangtze River China289 187 18.7.2016 Severe floods China240 – 3.11.2016 Boat carrying migrants capsizes Libyan Arab Jamahiriya228 – 15.7.2016 Monsoon floods India191 104 15.5.2016 Remnants of Cyclone Roanu bring torrential rains and flooding,

Arananayake landslideSri Lanka

178 – 21.9.2016 Overcrowded boat carrying migrants capsizes Egypt160 – 10.12.2016 Roof of a church collapses during a service Nigeria151 – 1.8.2016 Monsoon floods India150 – 20.11.2016 14 coaches of a passenger train derail India141 – 9.3.2016 River floods, flash floods, landslides Pakistan137 4887 14.4.2016 Earthquakes Japan122 – 21.7.2016 River floods, landslides Nepal117 618 6.2.2016 Earthquake Mw 6.4 Taiwan112 – 10.4.2016 Explosion and fire at a temple in firework display India



Table 9 Chronological list of all natural catastrophes in 2016

Floods

Date Country EventNumber of victims and amount of damage (where data available), in local currency and/or USD

24.1.–25.1. EcuadorEsmeraldas, San Lorenzo

Torrential rains cause flash floods 9 dead2000 homeless

3.2.–6.2. MexicoTamaulipas, Veracruz, Chihuahua

Flash floods caused by torrential rains – over 20 000 houses flooded

2 dead>3000 homeless

5.2.–24.2. IndonesiaCentral Java, West Sumatra, Bangka Belitung, Riau, Jambi

Floods, landslides – 1767 houses destroyed 14 dead>2000 homeless

10.2.–15.2. TanzaniaRufiji

Flash floods 3000 homeless

28.2. HaitiGrand’Anse,

River floods, flash floods – 10 000 houses flooded

1 dead, 4 missing2000 homeless

29.2.–1.3. AngolaLubango, Huíla

Flash floods 29 dead, 25 missing

8.3.–12.3. United StatesTX, LA, AR, MS

Severe flooding along the Sabine River basin on the Texas and Louisiana border – over 1000 buildings damaged or destroyed

5 deadUSD 333mn insured lossUSD 2.3bn total damage

9.3.–29.3. PakistanAzad Jammu and Kashmir, Punjab, Khyber Pakhtunkhwa, Gilgit-Baltistan

River floods, flash floods, landslides – 857 buildings damaged

141 dead127 injured

10.3.–11.3. BrazilSão Paulo

River floods (Pinheiros River), flash floods, landslides

30 dead24 injured

19.3.–23.3. ChinaJiangxi, Hunan, Guangdong, Guangxi, Guizhou

Floods, landslides – 1100 houses destroyed, 72 000 houses damaged

5 deadUSD 170mn total damage

2.4.–4.4. AfghanistanDaykundi, Ghazni, Uruzgan

Flash floods 30 dead

2.4.–7.4. EthiopiaJigjiga

Floods along Fafen River 28 dead80 injured

2.4.–8.4. PakistanKhyber Pakhtunkhwa

River floods, flash floods, landslides – 1200 buildings damaged

92 dead77 injured

4.4.–15.4. ArgentinaLa Paz, Entre Ríos, Santa Fe, Chaco, Corrientes

River floods – severe damage to agriculture 1 dead12 000 homeless>USD 50mn insured lossUSD 1bn total damage

8.4.–10.4. MalawiMzuzu, Karonga

Flash floods 12 dead2800 homeless

15.4.–21.4. Uruguay, ArgentinaArtigas, Colonia, Durazno, Paysandú, San José, Treinta y Tres, Montevideo

Inland floods, river floods along Cabayú Cuatia River, La Paz, Entre Rios

6 dead270 injured

16.4.–17.4. AfghanistanBaghlan, Samangan, Takhar, Badghis


16.4.–19.4. United StatesHouston, TX, CO

Flash and river floods in Houston region after torrential rains – over 1000 houses flooded

8 deadUSD 1.031bn insured lossUSD 2.7bn total damage




20.4.–24.4. AngolaLuanda

Flash floods 19 dead, 4 missing50 injured2400 homeless

22.4.–24.4. TanzaniaMorogoro, Kilosa, Kilombero, Malinyi

Remnants of Cyclone Fantala trigger inland flood – 315 houses destroyed, 3095 houses damaged

13 dead13 933 homeless

28.4.–30.5. KenyaNairobi, Turkana, Wajir, Marsabit

River floods along Garissa and Tana rivers, flash floods

4 dead6675 homeless

4.5.–11.5. ChinaZhejiang, Fujian, Jiangxi, Hubei, Hunan, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan

River floods. landslides – 5200 houses destroyed, 74 000 houses damaged


7.5.–8.5. RwandaDistricts of Gakenke (Nothern Province), Ngororero (Western Province), Muhanga (Southern Province)

Flash floods, landslides – 2317 houses destroyed

54 dead4000 homeless

9.5.–20.5. EthiopiaOromia, Bale, Southern Nations, Nationalities and People’s Region

River floods along Shabelle River, massive landslide in Kindo Didaye

100 dead

15.5.–19.5. Sri LankaColombo, Gampaha, Kegalle

Remnants of Cyclone Roanu bring torrential rains and flooding, Arananayake landslide – 691 houses destroyed,

89 dead, 102 missing50 injuredLKR 15.5bn (USD 104mn) insured lossUSD 1.2bn total damage

1.6.–28.6. Myanmar (Burma)Ayeyarwady, Bago, Sagaing

Monsoon floods – 280 houses destroyed, 5000 houses damaged


17.6.–24.6. IndonesiaPurworejo, Banjarnegara, Kebumen, Sukoharjo, Bahyumas and Rembang, Central Java Province

River floods, flash floods, landslides 43 dead, 19 missing

18.6.–23.6. ChinaHunan, Guizhou, Fujian

Monsoon floods 35 deadUSD 60mn insured lossUSD 1.5bn total damage

18.6.–23.6. ChinaJiangsu, Zheijiang, Anhui, Jiangxi, Gansu, Shaanxi, Qinghai, Hubei, Hunan, Guangxi

River floods 31 dead, 6 missingUSD 1.5bn total damage

21.6.–16.7. Burkina FasoOuagadougou, Kadiogo Province

River floods, flash floods 4 dead10 injured2500 homeless

22.6.–23.6. United StatesOH, IN, IL, WV, VA

Thunderstorms, hail, tornadoes, severe flash floods, river floods, landslides and mudslides in West Virginia – 1500 roads damaged or destroyed

23 deadUSD 100–300mn insured lossUSD 1bn total damage

27.6.–30.6. SudanSennar

Flash floods – 1160 houses destroyed, 1320 houses damaged

4000 homeless

30.6.–15.7. ChinaJiangsu, Anhui, Jiangxi, Henan, Hubei, Hunan, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan

Severe floods along Yangtze River 289 deadCNY 3bn (USD 432mn) insured lossUSD 22bn total damage



1.7.–2.7. IndiaChamoli, Pithoragarh, Uttarakhand State

Monsoon floods along Alaknanda and Mandakini rivers

61 dead

3.7.–6.7. PakistanChitral District, Khyber Pakhtunkhwa Province


4.7.–17.7. IndiaJorhat, Golaghat, Assam State

Monsoon floods 34 dead

7.7.–19.7. IndiaBhopal, Shajapur, Jabalpur, Satna, Harda, Madhya Pradesh State

Monsoon floods – 2360 houses destroyed, 17 236 homes damaged

37 dead, 9 missing2000 homeless

11.7.–23.7. MaliGao, Mopti Segou, Sikasso

River floods, flash floods 13 dead2100 homeless

13.7.–26.8. SudanKassala, North Darfur, Khartoum, Al Jazirah

Flash floods, river floods – >18 000 houses destroyed, >14 000 houses damaged

36 dead147 injuredUSD 10mn total damage

15.7.–11.8. NigerAgadez, Tahoua

Flash flood, river floods – 1700 houses destroyed


15.7.–20.8. IndiaBihar, Uttar Pradesh

Monsoon floods 228 dead

18.7.–21.7. ChinaHebei, Henan, Beijing, Tianjin, Shanxi, Inner Mongolia Region, Liaoning, Shandong

Severe floods – 125 000 houses damaged or destroyed

164 dead, 125 missingCNY 28.1bn (USD 4.047bn) total damage

21.7.–27.7. NepalPyuthan, Gulmi, Palpa, Makwanpur, Udaypur, Baglung, Banke, Rupandehi

River floods, landslides – 374 houses destroyed, 561 houses damaged

122 dead

24.7.–9.8. IndiaAssam

Monsoon floods 36 deadUSD 150mn total damage

1.8.–22.8. IndiaMaharashtra, Madhya Pradesh

Monsoon floods 151 deadUSD 300mn total damage

6.8.–7.8. PakistanKarachi, Hyderabad, Tando Allahyar, Mirpur Khas

Flash floods, inland floods 22 dead60 injured

8.8.–16.8. PhilippinesLlocos Sur, Bataan, Pampanga, Negros Occidental

Monsoon floods – 276 houses destroyed, 151 houses damaged

23 dead, 3 missing12 injuredPHP 665mn (USD 13mn) total damage

11.8.–31.8. United StatesLouisiana, Mississippi

Severe storms and flooding in Louisiana – 50 000 houses, 20 000 vehicles and 20 000 businesses damaged or destroyed, 100 000 people displaced, more than 30 000 people rescued from floodwaters

13 dead10 000 homelessUSD 3.1bn insured lossUSD 10bn total damage

29.8.–31.8. North KoreaNorth Hamgyong

Remnants of Typhoon Lionrock trigger floods along Timern River – 30 000 houses damaged or destroyed

138 dead, 400 missingUSD 61mn total damage

1.9.–20.9. NigeriaDutse, Jahun, Hadejia, Babura, Ringim, Gumel, Malammadori, Birninkudu

Seasonal river floods – 6637 houses destroyed


11.9. South AfricaGauteng

Flash floods 6 deadUSD 100mn total damage

20.9.–22.9. IndonesiaGarut

Flash floods, landslides 53 deadUSD 22mn total damage




21.9.–29.9. IndiaAndhra Pradesh, Telangana

Floods in Andhra Pradesh 28 deadINR 3bn (USD 44mn) insured lossINR 40bn (USD 589mn) total damage

28.9.–30.9. Canada, United StatesWindsor, Leamington, ON

Flash floods in Windsor, ON and Detroit, MI USD 108mn insured lossUSD 169mn total damage

3.10.–10.10. ThailandNakornsawan Province

River flood – 68 000 houses damaged 4 deadUSD 120mn total damage

9.10.–16.10. Viet NamHa Tinh, Nghe An, Quang Binh, Quang Tri, Thua Thien Hue

River floods 26 dead

18.10.–22.10. ColombiaChocó Department

River floods – San Juan River and Condoto rivers burst their banks

4 dead2200 homeless

27.10.–29.10. EgyptHurghada, Red Sea Governorate

Thunderstorms, flash floods, torrential rains 29 dead73 injured

3.11.–5.11. MexicoTamaulipas, Veracruz, Chihuahua

Thunderstorms, flash floods, hail – 20 000 houses damaged

2 dead3000 homeless

5.11.–8.11. HaitiCap-Haitien, Nord Department; Jérémie, Grand’Anse department

Inland river floods, flash floods, landslides 13 dead2 injured2780 homeless

7.11.–21.11. Dominican RepublicCabrera, María Trinidad Sánchez

River floods (Tío Marcos, Bajabonico and Angostura rivers) – damage to agriculture


9.11.–15.11. IndonesiaWest Java Province

River flood, flash floods – 5776 houses damaged

5 dead6373 homeless

26.11.–5.12. SpainMalaga, Cadiz, Huelva, Valencia

Floods, rainstorms 2 dead1 injuredEUR 60mn (USD 63mn) insured loss

21.12.–23.12. Viet NamBinh Dinh, Quang Ngai

River floods 24 dead

27.12. Congo, Democratic Republic of (DRC)Boma

Floods along Kalamu River 50 dead


Storms


1.1.–5.1. Iran Blizzards, heavy snowfall in 21 provinces 84 injured5.1.–8.1. United States

CAMultiple low pressure systems bring rainstorms, mudslides, debris flow, floods, 1 tornado

USD 25–100mn insured loss

22.1.–24.1. United StatesVA, MD, NJ, PA, KY, NC, GA, NY, TN, DE, WV, SC, DC, OH, MA, CT, RI

Winter storm Jonas, strong winds, heavy snowfall, storm surge, coastal flooding, record snow fall in Baltimore, Maryland and New York City, 13 000 flights cancelled

58 deadUSD 100–300mn insured loss

23.1.–24.1. Japan Winter storm, heavy snowfall 8 dead610 injured

29.1.–30.1. United Kingdom Winter storm Marita 1 deadGBP 47mn (USD 58mn) insured loss

31.1.–1.2. United StatesLos Angeles, Ventura (CA)

Thunderstorms, flash floods, landslides USD 25–100m insured loss

7.2. United Kingdom, France Winter storm Ruzica-Susanna USD 168m insured loss13.2.–15.2. United States

NY, MA, NJ, CT, RI, PA, NH, MD, VT, DC

Winter storm, heavy snowfall, flooding USD 300–600mn insured loss

19.2.–20.2. United StatesIL, MI

Thunderstorms, strong winds USD 100–300mn insured loss

20.2.–22.2. Fiji, Tonga Cyclone Winston Cat 5 with winds up to 295 km/h – 11 989 houses destroyed, 18 380 homes damaged, severe damage to sugar plantations

44 dead83 injuredUSD 50mn insured lossUSD 1.351bn total damage

22.2.–25.2. United States, CanadaUS: TX, NC, LA, FL, GA, VA, NY, SC, PA, MA, AL, CT, MS, DC, DE, Canada: New Brunswick, Ontario, Quebec

Thunderstorms, 50 recorded tornadoes (1 EF3 in Pensacola, FL – 1 EF3 tornado in Appomattox County, VA), hail in southern and eastern states

10 dead56 injuredUSD 600mn – 1bn insured lossUSD 1.03bn total damage

1.3. United Kingdom, Ireland Winter storm Aloisia 3 deadEUR 85mn (USD 90mn) insured loss

3.3.–9.3. ChinaGuizhou, Fujian, Yunnan, Kinjiang

Thunderstorms, torrential rains USD 200mn total damage

5.3.–11.3. United StatesLA, TX, CA, MS, AR, TN, OK

Thunderstorms, flooding in California, hail, mudslides

5 deadUSD 300mn–600mn insured loss

8.3.–11.3. United Arab Emirates, Oman Thunderstorms, hail, widespread floods USD 100mn insured lossUSD 300mn total damage

13.3.–14.3. United StatesSC, AR, NC

Thunderstorms, hail, tornadoes USD 100mn–300mn insured loss

13.3.–15.3. United StatesIL, WA, CA


17.3.–18.3. United StatesTX, LA, MS, AR, FL, AL

Thunderstorms, large hail in Forth Worth and Arlington in TX

1 deadUSD 600mn–1bn insured lossUSD 1.2bn total damage

27.3. United StatesIN

Thunderstorms, hail USD 25mn–100m insured loss

27.3.–29.3. United Kingdom Winter storm Jeanne 1 deadGBP 118mn (USD 146mn) insured loss

30.3.–1.4. United StatesTX, OK, MS, AR, AL, LA, KS

Thunderstorms, hail, tornadoes, flash floods 7 deadUSD 100mn–300mn insured loss

2.4.–3.4. United StatesIN, OH, NJ, IL, PA, MD, VA, NY, DE, DC

Thunderstorms, hail USD 100mn–300mn insured loss




15.4. UruguayDolores

EF3 tornado – 251 buildings destroyed, 13 912 buildings damaged (70% of buildings of Dolores)


19.4.–24.4. Myanmar (Burma)Mandalay, Sagaing, Magway, Shan, Chin

Thunderstorms, large hail, flash floods – 7500 houses destroyed


20.4.–25.4. ChinaHubei, Henan, Shaanxi, Guangxi, Guizhou, Shandong

Thunderstorms, large hail 49 injuredCNY 1.4bn (USD 202mn) total damage

25.4.–28.4. United StatesTX, KS, MO, IN, WV, OK, IL, NC, MS

Thunderstorms, hail, tornadoes 6 dead19 injuredUSD 600mn–1bn insured loss

29.4. BangladeshSunamganj

Nor'wester – 200 houses damaged 1 dead50 injured

29.4.–3.5. United StatesTX, AR, VA, IN, NC, MD, OK, GA, MO, IL, WV

Thunderstorms, large hail, tornadoes, flash floods

6 deadUSD 1bn–3bn insured lossUSD 2.4bn total damage

3.5.–5.5. IndiaUjjain, MP

Thunderstorms, large hail, torrential rains – tents erected for religious festival uprooted

8 dead81 injured

7.5.–10.5. United StatesNE, KY, TX, OK, CO, TN, KS

Thunderstorms, hail, tornadoes 2 dead10 injuredUSD 600mn–1bn insured loss

11.5.–12.5. United StatesMO, TX, NE, IL

Thunderstorms, hail, tornadoes USD 600mn–1bn insured loss

16.5.–19.5. United StatesTX

Thunderstorms, hail USD 100mn–300m insured loss

17.5.–23.5. Bangladesh Cyclone Roanu, storm surge – 23 940 houses destroyed, 216 771 houses damaged


21.5.–28.5. United StatesTX, MT, KS, MO, CO

Thunderstorms, tornadoes, hail 1 deadUSD 600mn–1bn insured lossUSD 1.1bn total damage

27.5.–7.6. Germany, France, Switzerland, Belgium, Luxembourg, Poland, Austria, Romania

River and flash floods caused by thunderstorms and heavy rains due to low-pressure systems Elvira and Friederike

17 dead35 injuredEUR 2.736bn (USD 2.886bn) insured lossEUR 3.8bn (USD 4.0bn) total damage

29.5.–2.6. United StatesTX

Thunderstorms, floods, tornadoes 15 deadUSD 100mn–300m insured loss

1.6.–2.6. PakistanIslamabad, Rawalpindi, Khyber Pakhtunkhwa

Thunderstorms, torrential rains, flash floods – glass roof of a shopping mall collapses

34 dead191 injured

2.6. ChinaQinqhai

Thunderstorms, hail 2200 homelessUSD 60mn total damage

3.6.–7.6. AustraliaQLD, NSW, VIC, TAS

Winter storm (East Coast Low) brings wind, storm surge, coastal erosion and flood damage

4 deadAUD 422mn (USD 305mn) insured loss

6.6.–7.6. United StatesDenver, CO


16.6.–18.6. United StatesVA, GA, AL, SC

Thunderstorms, hail, torrential rains USD 100mn–300mn insured loss

16.6.–18.6. United StatesND, MN, SD


23.6. ChinaYancheng, Jiangsu

Thunderstorms, large hail, EF4 tornado (Jiangsu tornado)


23.6. NetherlandsNorth Brabant, Limburg

Thunderstorms, hailstorm – severe crop damage

EUR 500mn (USD 527mn) insured lossEUR 800mn (USD 844mn) total damage

24.6.–25.6. GermanyWestfälische Provinzial, Provinzial Rheinland, Bavaria

Thunderstorms, large hail, flash floods (depressions Lea, Marine, Neele)

92 injuredEUR 240mn (USD 253mn) insured loss



28.6.–30.6. CanadaOkotoks, Claresholm, Edmonton, Calgary (AB), SK, MB

Thunderstorms, large hail, flash floods, 1 tornado

CAD 86mn (USD 64mn) insured lossCAD 110mn (USD 82mn) total damage

5.7.–7.7. United StatesMN, TN, KY, WI

Thunderstorms, hail, flash floods in TN USD 100mn–300mn insured loss

7.7.–9.7. United StatesCO, MI, NC, TN

Thunderstorms, large hail USD 100mn–300mn insured loss

8.7.–12.7. Philippines, Taiwan, China Typhoon Nepartak 111 deadUSD 1bn total damage

13.7.–15.7. United StatesCO, OK, IL, AR, MO, KS


15.7.–16.7. CanadaLethbridge, Calgary, Arbour Lake (AB), SK

Thunderstorms, large hail, flash floods CAD 75mn (USD 56mn) insured loss

18.7.–20.7. CanadaAlberta, Manitoba, Saskatchewan

Thunderstorms with winds up to 122 km/h, tornadoes, large hail, flash floods

CAD 99mn (USD 74mn) insured loss

20.7.–21.7. United StatesMN


24.7.–26.7. South AfricaCape Town, Durban

Thunderstorms, flash floods – >2300 buildings flooded

7 deadZAR 2bn (USD 146mn) insured loss

30.7.–1.8. United StatesMD, NJ, NY, PA, VA

Thunderstorms, flash floods in Maryland and New Jersey, hail

2 deadUSD 100mn–300mn insured loss

30.7.–1.8. CanadaAlberta, Saskatchewan, Manitoba

Thunderstorms with winds up to 113 km/h, large hail, 3 tornadoes, flash floods in the Prairie


31.7.–3.8. Philippines, China, Viet Nam Typhoon Nida USD 150mn total damage2.8.–5.8. Mexico, Belize Hurricane Earl, storm surge, floods 67 dead

USD 25mn insured lossUSD 250mn total damage

6.8.–7.8. Macedonia, Skopje

Cloudburst triggers flash floods 22 dead77 injuredUSD 50mn total damage

9.8. PakistanBannu

Thunderstorms 2 dead59 injured

17.8.–30.8. Japan, China Typhoon Lionrock 79 dead18.8.–22.8. China, Vietnam Tropical storm Dianmu 17 dead

USD 270mn total damage24.8.–25.8. United States

IN, OHThunderstorms, tornadoes, hail, flash floods 20 injured

USD 25–100m insured loss31.8.–4.9. United States

FL, GA, NC, SC, VA, DEHurricane Hermine (Cat 1), storm surge USD 100mn–300mn insured loss

2.9. IranGolestan

Thunderstorms, flash floods – 900 buildings damaged

4 dead2000 homeless

8.9.–13.9. ChinaShandong, Henan, Fujian

Thunderstorms. large hail, floods – 2000 houses damaged, crop damage

USD 175mn total damage

14.9.–16.9. China, Taiwan, Philippines Typhoon Meranti 44 dead<USD 400m insured lossUSD 2.5bn total damage

19.9.–23.9. United StatesWI, MN, IA

Thunderstorms, hail, tornadoes, river floods in the Cedar River basin, Shell Rock River, flash floods

USD 100–300m insured loss

23.9.–28.9. China, TaiwanChina: Zhejiang, Fujian, Jiangxi

Typhoon Megi, floods, landslides 10 dead, 17 missing625 injuredUSD 951mn total damage




28.9.–8.10. United States, Haiti, Barbados, Saint Lucia, Saint Vincent and The Grenadines, Cuba, Bahamas, Dominican Republic, Colombia, Jamaica

Hurricane Matthew, storm surge, wind damage, inland river floods and flash floods in eastern North Carolina

606 dead, 128 missing150 000 homelessUSD 4bn insured lossUSD 12bn total damage

3.10.–6.10. Japan, South Korea Typhoon Chaba, storm surge 9 dead, 4 missingUSD 200mn insured lossUSD 800mn total damage

9.10.–11.10. CanadaSydney, Cape Breton, Burgeo (Newfoundland and Labrador), Nova Scotia

Remnants of Hurricane Matthew bring wind and inland flood damage from heavy rainfall and winds. Record rainfall in Sydney, Nova Scotia, with over 1000 houses suffering flood damage


13.10.–15.10. Viet Nam Tropical storm Aere 31 deadUSD 100mn total damage

16.10.–19.10. Philippines, China Typhoon Sarika with winds up to 210 km/h – 2421 houses destroyed, 16 956 houses damaged


19.10.–21.10. Philippines, China Typhoon Haima with sustained winds of up to 225 km/h and gusts of 310 km/h, storm surge – 14 564 houses destroyed, 79 371 houses damaged

15 dead17 injuredUSD 1.083bn total damage

19.10.–22.10. Taiwan, Japan Typhoon Malakas 1 deadUSD 300mn total damage

24.10. MozambiqueMaputo

Thunderstorms, large hail – 400 houses destroyed

12 dead200 injured1500 homeless

11.11. AustraliaMildura, VIC, SA, NSW

Thunderstorms, large hail, heavy rains – extensive damage to vehicles and crops (vineyards, almonds, stone fruit, wheat)

AUD 272mn (USD 197mn) insured loss

21.11.–26.11. Nicaragua, Costa Rica, Panama

Hurricane Otto (Cat 2) with winds up to 175 km/h, storm surge , inland flash floods, landslides

18 dead2054 homelessUSD 1mn insured lossUSD 50mn total damage

28.11.–1.12. United StatesTN, AL, GA, SC, MS, LA, NC

Thunderstorms, tornadoes 8 dead33 injuredUSD 100mn–300mn insured loss

12.12.–14.12. IndiaTamil Nadu, Andaman, Nicobar Islands

Cyclone Vardah, flash floods 12 deadUSD 52mn insured lossUSD 1bn total damage

25.12.–28.12. PhilippinesCatanduanes Island, Calabarzon, Mimaropa, Region V

Typhoon Nock-Ten (Nina) with sustained winds of 185 km/h and gusts of 255 km/h – 85 229 houses destroyed, 228 538 houses damaged

13 dead, 21 missingPHP 12.115bn (USD 244mn) total damage


Earthquakes

Date Country EventNumber of victims and amount of damage (where data available),in local currency and/or USD

4.1. India, Bangladesh, BhutanImphal, Manipur

Earthquake Mw 6.7 21 dead350 injuredUSD 60mn total damage

6.2. TaiwanKaohsiung, Tainan

Earthquake Mw 6.4 – 19 buildings destroyed, 565 buildings damaged, including collapse of a 17-storey block and damage in an industrial park

117 dead551 injured770 homeless

13.4. Myanmar (Burma), India, BangladeshMawlaik

Earthquake Mw 6.9 2 dead182 injured

14.4.–16.4. JapanKumamoto, Kyushu

Earthquakes – 8549 buildings destroyed, 162 969 buildings damaged

137 dead2054 injuredJPY 570bn (USD 4.9bn) insured lossUSD 25bn to 30bn total damage

16.4. EcuadorMuisne

Earthquake Mw 7.8 – 1100 buildings destroyed, 36 149 buildings damaged

664 dead, 9 missing6274 injured140 000 homelessUSD 501mn insured lossUSD 4bn total damage

18.5. EcuadorMompiche, Esmeraldas

Earthquake Mw 6.7 (aftershock of April 16 event) – 32 buildings destroyed

1 dead162 injured

18.5. ChinaShimen

Earthquake Mw 4.9 2 dead>5000 homelessUSD 60mn total damage

24.8. ItalyAmatrice, Accumoli, Pescara del Tronto

Earthquake Mw 6.2 299 dead368 injured2500 homelessEUR 66mn (USD 69mn) insured loss

10.9. Tanzania, UgandaNsunga, Bukoba

Earthquake Mw 5.9 – 2500 houses destroyed, 15 400 houses damaged, 1700 government buildings damaged

21 dead447 injured117 721 homeless

11.9. Macedonia Skopje

Earthquake Mw 5.3, aftershocks 137 injured

21.10. JapanTottori

Earthquake Mw 6.6 7 injuredJPY 5.8bn (USD 50mn) insured lossJPY 16bn (USD 137mn) total damage

26.10.–30.10. ItalyNorcia

Series of earthquakes, Mw 5.5 and 6.1 in Visso on 26 October and Mw 6.6 in Norcia on 30 October

38 injuredEUR 125mn (USD 132mn) insured loss

14.11. New ZealandCulverden, Kaikoura

Earthquake Mw 7.8 triggers tsunami and coastal uplifting

2 dead57 injuredUSD 1.7-2.4bn insured lossUSD 3.9bn total damage

7.12. IndonesiaSigli City, Aceh Province

Earthquake Mw 6.5 – 18 752 houses damaged 103 dead755 injured85 256 homeless

8.12. ChinaShihezi, Xinjiang

Earthquake Mw 6.0 CNY 935mn (USD 135mn) total damage

30.12. PhilippinesSurigao City

Earthquake Mw 6.7 8 dead202 injured



Drought, bush fires, heat waves

Date Country EventNumber of victims and amount of damage (where data available),in local currency and/or USD

1.1.–19.6. CyprusNicosia, Larnaca, Famagusta

Drought, wildfires EUR 181mn (USD 191mn) total damage

1.1.–30.6. India Drought USD 400mn insured lossUSD 3bn total damage

1.1.–31.12. United States Drought in North East and South East USD 1.5bn total damage1.1.–31.12. Zimbabwe Severe drought >USD 500mn total damage6.1.–12.1. Australia

Waroona, Yarloop, Preston Beach, WA

Yarloop bushfires – 181 buildings destroyed, 69 000 ha of land burnt

2 dead4 injuredAUD 71mn (USD 52mn) insured loss

10.1.–12.1. South AfricaNgaka Modiri Molema

Heatwave with temperatures up to 43 degrees Celsius

21 dead

1.2.–31.12. Bolivia Severe drought – damage to agriculture >USD 100mn total damage4.2.–20.8. China Drought USD 200mn insured loss

USD 4bn total damage13.4.–22.4. India

Orissa, Telengana, Andhra Pradesh

Heat wave with record temperatures up to 51 degrees Celsius

300 dead

2.5.–9.5. CanadaFort McMurray, Alberta

Fort McMurray wildfires – 2400 houses destroyed, 590 000 ha of land burnt

CAD 3.731bn (USD 2.782bn) insured lossCAD 5.3bn (USD 3.953bn) total damage

1.7 France Drought – crop damage EUR 240 mn (USD 253 mn) insured losses22.7.–26.7. United States

CAWildfires (Sand fire) USD 25mn–100m insured loss

6.8.–8.8. JapanYamanashi, Kawanehoncho, Shizuoka

Heat wave 3 dead490 injured

8.8.–13.8. PortugalFunchal, Calheta, Madeira

Wildfires – 154 houses destroyed, 233 houses damaged, 6000 ha of land burnt

EUR 157mn (USD 166mn) total damage

22.11.–27.11. IsraelHaifa

Wildfires, urban fires (believed to be man-made) – > 600 houses destroyed or damaged, 13 000 ha of land burnt


28.11.–30.11. United StatesGatlinburg, Pigeon Forge, TN

Man-caused fires spread to forest areas due to dry conditions (Chimney Tops 2 Fire) – 2121 houses and 85 commercial buildings destroyed, 85 houses and 5 commercial buildings damaged

14 dead191 injuredUSD 600mn–1bn insured lossUSD 1.2bn total damage

Cold frost


1.1.–3.1. Poland Cold spell 21 dead1.1.–12.1. Ukraine Cold spell 37 dead20.1.–26.1. Taiwan, Thailand Cold wave 100 dead24.1.–27.1. China, North Korea, Taiwan Frost damage from winter weather, heavy

snowfall70 injured>USD 800mn total damage

18.9.–21.9. AustraliaWestern Australia, WA

Frost in freezing temperatures damages crops AUD 140mn (USD 101mn) total damage


Hail


23.3. United StatesDallas, Fort Worth, Plano (TX)

North Texas hailstorm, thunderstorms USD 1bn–3bn insured lossUSD 2.1bn total damage

8.4. BangladeshBaralekha, Moulvibazar

Hailstorm – 2800 houses damaged >2000 homeless

10.4.–15.4. United StatesSan Antonio, TX, FL

Severe hailstorm in San Antonio, TX USD 1bn–3bn insured lossUSD 3.5bn total damage

22.7. CanadaMoose Jaw, SK

Hailstorm CAD 75mn (USD 56mn) insured loss

28.7.–29.7. United StatesCO, WY

Thunderstorms, severe hail damage in CO, hailstorm in Wyoming

USD 1bn–3bn insured loss

4.11.–6.11. United StatesTX, NM

Hailstorm in El Paso, TX, thunderstorms USD 300mn–600mn insured loss

Other natural catastrophes

Date Country EventNumber of victims and amount of damage (where data available), in original currency and/or USD

23.5. YemenLasbah, Al-Shamayaten District, Taiz Governorate

Heavy rains trigger landslide 24 dead

Table 9 uses loss ranges for US natural catastrophes as defined by Property Claims Services. For Canada loss estimates, the data is from CatIQ.




Table 10 Chronological list of all man-made disasters in 2016

Aviation disasters


24.2. NepalTirkhe Dungha, Myagdi

Tara Air Viking Air DHC-6 Twin Otter 400 craft crashes in poor visibility

23 dead

15.3. PeruPastaza

Aviación del Ejército Ecuatoriana IAI Arava 201 crashes en route

22 dead

19.3. RussiaRostov-On-Don Airport

Flydubai Boeing 737–8KN (WL) crashes on landing

62 dead

26.3. Space JAXA ASTRO-H (Hitomi) X-ray astronomy satellite disintegrates in orbit after spinning out of control


19.5. Egypt200 km north of the Egyptian coast line

EgyptAir Airbus A320-232 crashes in unknown circumstances

66 dead

3.8. United Arab EmiratesDubai

Emirates Boeing 777-31H catches fire shortly after crash landing

1 dead

28.10. United StatesChicago

American Airlines B767-300ER catches fire shortly ahead of take-off

28.11. ColombiaMedellín

LaMia Avro RJ.85 crashes en route to Medellín after running out of fuel

71 dead

1.12. Space Roscosmos Progress-MS-4 cargo spacecraft lost due to launch failure

7.12. PakistanHavelian

PIA ATR 42-500 crashes en route to Islamabad 47 dead

25.12. RussiaAdler

Russian Air Force Tupolev 154B-2 crashes shortly after take-off

92 dead

Collapse of buildings / bridges

Date Country Event Number of victims

8.3. NigeriaLagos

Five-storey building collapses 35 dead

31.3. IndiaKilkata

Bridge collapses 23 dead

3.8. IndiaRaigad District, Maharashtra

Buses and vehicles plunge into Savitri River after bridge collapses

24 dead, 18 missing

10.12. NigeriaUyo, Akawa Ibom

Church roof collapses during a service 160 dead200 injured

Major fires, explosions

Date Country EventNumber of victims and amount of damage (where data available), in local currency and USD

1.1. PhilippinesManila

Fire at a shanty town triggered by firecrackers during New Year’s Eve celebrations

3000 homeless

5.1. PakistanLahore

Gas leak from an ice factory 150 injured



8.1. JapanChita

Explosion at a steel plant

15.1. CanadaFort McMurray, Alberta

Fire and explosions at an oil sand facility 1 dead1 injured

21.1. HungaryZala

Fire at a petrochemicals plant

1.2. RussiaSharypovo, Krasnoyarsk

Fire at a coal power plant

8.2. GermanyPaderborn

Fire at a meat factory 2 injured

15.2. ColombiaAntioquia

Fire at a hydroelectric plant

16.2. RussiaYaroslavl

Gas explosion in an apartment block 39 dead

5.3. United StatesTexas

Fire and explosion at an oil refinery

28.3. GermanyLohne

Fire at a meat processing plant

31.3. FrancePaluel

Steam generator falls, damages nuclear power plant

2.4. QatarDoha

Fire at a shopping mall

10.4. IndiaParavur, Kollam

Explosion and fire at a temple in a fireworks display

112 dead350 injured

21.4. MexicoCoatzacoalcos, Veracruz

Explosion at a petrochemicals plant 32 dead136 injured

21.4. United StatesChicago

Fire at a furniture warehouse

29.4. KenyaNairobi

Residential six-storey building collapses in bad weather; building had been declared unsafe and was illegally occupied

49 dead, 21 missing135 injured

9.5. South KoreaChungcheongnam

Fire at a power station

29.5. PakistanTehsil Fateh Jang, Punjab

Silo collapses at a cement company

15.6. CanadaBritish Columbia

Damage at a gas pipeline during flooding

18.6. PhilippinesIsabela City

Fire at a residential area – 560 houses destroyed

2000 homelessPHP 105m (USD 2m) total damage

21.6. PhilippinesZamboanga City

Fire at a residential area – 600 houses destroyed

5 injured2000 homelessPHP 5m total damage

27.6. United StatesJackson County, Mississippi

Fire and explosion at a gas plant

7.7. IranBandar Imam Khomeini

Fire at a petrochemicals plant

14.7. RussiaKhanty-Mansi Autonomous

Fire and explosion at a gas plant

16.7. RussiaUfa

Explosion at an oil refinery 8 dead

16.7. SpainSeville

Fire at a food processing plant

20.7.–22.7. CanadaMaidstone, Saskatchewan

Pipeline bursts and spills 225 000 litres of heavy oil and diluent into the North Saskatchewan River

23.7. MadagascarAmbalavato, Ikalamavony

Fire at a house party 38 dead




2.8. IndiaBellary, Karnataka

Accident at a steel plant

11.8. ChinaDangyang, Hubei

Explosion at a power plant 21 dead5 injured

11.8. United StatesSt. Clair County, Michigan

Fire and explosion at a power plant

3.9. EthiopiaAddis Ababa

Fire at a prison 23 dead

10.9. BangladeshGazipur

Explosion and fire at a packaging factory 31 dead, 8 missing

30.9. GermanyBochum

Fire at a hospital 2 dead16 injured

10.10. ChinaWenzhou, Zhejiang Province

Four adjacent residential buildings collapse 22 dead6 injured

17.10. GermanyLudwigshafen

Explosion at a chemical plant 1 dead, 6 missing7 injured

17.10. IndiaBhubaneswar

Fire at a hospital 21 dead100 injured

24.10. ChinaXinmin, Shaanxi

Explosion at a prefabricated house in a residential complex – 5 buildings destroyed, 58 buildings damaged

14 dead147 injured

13.11. PhilippinesMandaluyong City

Fire at residential establishments – 500 houses damaged

2 dead2000 homeless

24.11. ChinaFengcheng, Jiangxi

A construction platform for a power plant collapses

74 dead

26.11. BahrainSitra

Fire and explosion at a refinery

1.12. ItalyPavia

Fire and explosion at an oil refinery

2.12. United StatesOakland

Fire at a two-story warehouse during a party 36 dead2 injured

5.12. PakistanKarachi

Fire at a hotel 12 dead75 injured

20.12. MexicoTultepec, Mexico City

Explosion at a fireworks market 33 dead

Maritime disasters

Date Country Event Number of victims (where data available)

1.1. IndiaMumbai

Damage to oil rig during piling operations

5.1. Mediterranean Sea, TurkeyAyvalik

Boat carrying migrants capsizes 9 dead, 13 missing

5.1. TurkeyMarmaris

Fire on two recreational boats

6.1. ChinaSouth China Sea

Damage to oil rig during jacking operations

8.1. Indian Ocean, SomaliaSanag

Boat carrying migrants capsizes 106 dead

21.1. Turkey, Mediterranean SeaFoca, Izmir

Boat carrying migrants capsizes 12 dead, 20 missing

22.1. GreeceKalolimnos




26.1. DenmarkNorth Sea

Accident at a drilling platform

28.1. TurkeySamos


30.1. Turkey, Mediterranean SeaAyvacik


31.1. United StatesLouisiana

Bulk carrier collides with barge on the Mississippi River, a towing vessel and two other facility structures

1.2. IraqPersian Gulf

Damage to oil rig during jacking operations

7.2. Gulf of MexicoBay of Campeche

Fire and explosion at a drilling platform

8.2. Canadaoff Nova Scotia

Damage at a drilling platform during a storm

9.2. Mediterranean SeaAegean coast of Turkey


28.2. GhanaJubilee Field

Turret failure at a floating production, storage and offloading (FPSO) vessel

6.3. Turkey, Mediterranean SeaDidim


18.3. TaiwanKeelung

Container runs aground

30.3. Libyan Arab Jamahiriya, Mediterranean SeaZawiya


17.4. Myanmar (Burma), Indian OceanSittwe


1.5. AustraliaSouth China Sea

Damage to oil rig

8.5. NigeriaNiger Delta

Damage at a drilling platform

27.5. Libyan Arab Jamahiriya, Mediterranean Sea


3.6. Greece, Mediterranean SeaOff Crete


1.9. SpaceCape Canaveral Air Force Station

Explosion destroys SpaceX Falcon 9 rocket and Amos-6 satellite during static-fire test

1.9. GermanyHamburg

Explosion and fire on a containership

18.9. ThailandAyutthaya

Overcrowded double-decker boat capsizes on Chao Phraya River after hitting a bridge

28 dead33 injured

21.9. EgyptRosetta

Overcrowded boat carrying migrants capsizes 178 dead

1.10. Yemen Cargo vessel sinks15.10. Myanmar (Burma) Ferry capsizes on the Chindwin River 73 dead1.11. Pakistan

GadaniExplosion and fire on an oil tanker at a shipbreaking yard

26 dead59 injured

2.11. Philippine SeaBatam, Indonesia

Overcrowded boat carrying migrants capsizes 18 dead, 44 missing

3.11. Libyan Arab Jamahiriya Boat carrying migrants capsizes 240 dead4.11. Indonesia Boat carrying migrants capsizes 54 dead17.11. Libyan Arab Jamahiriya Boat carrying migrants capsizes 100 missing7.12. Indian Ocean

off SocotraCargo vessel sinks 40 missing



Mining accidents


22.1. South AfricaRustenburg

Fire at a mining company 4 dead

25.2.–28.2. RussiaVorkuta, Komi Republic

Three gas explosions at Severnaya coal mine 36 dead

17.7. CanadaSaskatoon

Equipment collapse at a potash mine

27.9. ChinaShizuishan City

Explosion at an illegal coal mine 18 dead, 2 missing

31.10. ChinaLaisu, Chongqing

Gas explosion at a coal mine 31 dead

29.11. ChinaQitaihe, Heilongjiang

Explosion at a coal mine 21 dead

3.12. ChinaChifeng, Inner Mongolia

Explosion at a coal mine 17 dead, 15 missing

29.12. IndiaGodda, District, Jharkhand

Landslide at a coal mine 16 dead, 6 missing

Rail disasters (incl. cableways)


19.1. ItalyCagliari

Two metro trains collide due to signal failure 70 injured

9.2. GermanyBad Aibling, Rosenheim

Two commuter trains collide head-on 12 dead89 injured

8.4. Costa RicaSan Jose

Two passenger trains collide head-on 245 injured

23.6. South AfricaLamontville, Durban

Two trains collide head-on 121 injured

12.7. ItalyCorato, Puglia

Two passenger trains collide head-on 23 dead52 injured

15.9. PakistanMultan, Punjab

Passenger train crashes into a freight engine 6 dead150 injured

29.9. United StatesHoboken, NJ

Commuter train fails to stop, derails and hits a wall at NJT Hoboken Terminal

1 dead110 injured

21.10. CameroonEséka, Centre Region

Passenger train crashes 75 dead550 injured

20.11. IndiaPukhrayan, UP

14 coaches of a passenger train derail 150 dead260 injured

25.11. IranSemnan

Two passenger trains collide; four carriages derail and two catch fire

49 dead103 injured

28.12. IndiaKanpur, UP

Passenger train derails 2 dead68 injured


Miscellaneous


15.1. Burkina FasoOuagadougou

Series of coordinated terrorist attacks at a restaurant and a hotel

30 dead55 injured

20.1. PakistanCharsadda

Mass shooting and suicide bombing at Bacha Khan University

20 dead60 injured

22.1. SomaliaMogadishu

Car bomb explosion outside a restaurant 20 dead17 injured

25.1. CameroonBodo

Suicide bombing at a market 28 dead65 injured

9.2. Hong Kong Riots over removal of illegal street stalls during New Year celebrations

90 injured

13.3 Turkey Ankara

Car bomb explosion in a crowded area 34 dead125 injured

22.3. BelgiumBrussels

Suicide bombing at Brussels airport and a metro station 34 dead260 injured

12.6. United StatesOrlando, FL

Mass shooting at a nightclub 49 dead53 injured

28.6. TurkeyIstanbul

Terrorist attack at Istanbul airport 41 dead239 injured

28.6. ChinaShenxian, Shandong

Leak at chemical plant 131 injured

1.7. BangladeshDhaka

Mass shooting at a restaurant 24 dead, 2 missing50 injured

14.7. FranceNice

Lorry ploughs through crowd during Bastille Day celebrations 84 dead202 injured

8.8. PakistanQuetta

Suicide bombing at a hospital 93 dead130 injured

20.8. TurkeyGaziantep

Suicide bombing at a wedding 57 dead66 injured

2.9. PhilippinesDavao City

Explosion at an open market 14 dead70 injured

1.10. EthiopiaBishoftu

Stampede at a religious festival 52 dead

15.10. IndiaVaranasi

Stampede at a religious gathering 24 dead20 injured

9.11. United KingdomCroydon

Tram derails 7 dead51 injured

10.12. TurkeyIstanbul

Two bomb explosions outside a football stadium 44 dead150 injured

11.12. EgyptCairo

Two bomb explosions outside a cathedral and a nearby church

27 dead




Table 11 The 40 most costly insurance losses (1970–2016)

Insured loss1

(in USD mn, indexed to 2016) Victims2 Start date Event Country/region

80 699 1836 25.8.2005 Hurricane Katrina, storm surge, damage to oil rigs US, Gulf of Mexico37 344 18 451 11.3.2011 Earthquake (Mw 9.0) triggers tsunami Japan30 141 237 24.10.2012 Hurricane Sandy, storm surge US, Caribbean, Canada27 368 65 23.8.1992 Hurricane Andrew, floods US, Bahamas25 456 2982 11.9.2001 Terror attack on WTC, Pentagon, other buildings US24 773 61 17.1.1994 Northridge earthquake (Mw 6.7) US22 577 193 6.9.2008 Hurricane Ike, floods, damage to oil rigs US, Caribbean, Gulf of Mexico17 072 185 22.2.2011 Earthquake (Mw 6.1), aftershocks New Zealand16 417 119 2.9.2004 Hurricane Ivan, damage to oil rigs US, Caribbean, Venezuela16 005 815 27.7.2011 Heavy monsoon rains, extreme flooding Thailand15 447 53 19.10.2005 Hurricane Wilma, torrential rains, flooding US, Mexico, Caribbean13 199 34 20.9.2005 Hurricane Rita, floods, damage to oil rigs US, Gulf of Mexico11 498 123 15.7.2012 Drought in the Corn Belt US10 033 36 11.8.2004 Hurricane Charley US, Caribbean, Gulf of Mexico

9950 51 27.9.1991 Typhoon Mireille/No. 19 Japan8852 71 15.9.1989 Hurricane Hugo US, Caribbean8804 562 27.2.2010 Earthquake (Mw 8.8) triggers tsunami Chile8577 95 25.1.1990 Winter storm Daria France, UK, Belgium, NL et. al.8356 110 25.12.1999 Winter storm Lothar Switzerland, UK, France, et. al.7789 321 22.4.2011 Major tornado outbreak; 349 tornadoes, hail US7522 177 20.5.2011 Tornado outbreak, winds up to 405 km/h, hail US7057 54 18.1.2007 Winter storm Kyrill, floods Germany, UK, NL, Belgium et. al.6546 22 15.10.1987 Storm and floods in Europe France, UK, NL, et. al.6388 50 26.8.2004 Hurricane Frances US, Bahamas6062 51 22.8.2011 Hurricane Irene, floods US, Canada, Caribbean5820 26 22.9.1999 Typhoon Bart/No 18 Japan5695 600 20.9.1998 Hurricane Georges, floods US, Caribbean5 649 64 25.2.1990 Winter storm Vivian Switzerland, Germany5502 – 4.9.2010 Earthquake (Mw 7.0), over 300 aftershocks New Zealand4895 3034 13.9.2004 Hurricane Jeanne; floods, landslides US, Caribbean4890 43 5.6.2001 Tropical storm Allison; heavy rain, floods US5000 137 14.4.2016 Earthquakes Japan4555 45 6.9.2004 Typhoon Songda/No. 18 Japan, South Korea4259 25 27.5.2013 Floods Germany, Czech Republic, et. al.4180 51 2.5.2003 Thunderstorms, tornadoes, hail, flash floods US4066 78 10.9.1999 Hurricane Floyd, heavy rain, floods US, Bahamas4000 734 6.10.2016 Hurricane Matthew US, Caribbean 3954 – 27.7.2013 Hailstorms Germany, France3946 77 1.10.1995 Hurricane Opal, floods US, Mexico, Guatemala3893 6 434 17.1.1995 Great Hanshin earthquake in Kobe (Mw 6.9) Japan

Note: Mw = moment magnitude scale.


20 Property and business interruption, excluding liability and life insurance losses; US natural catastrophe figures based on Property Claim Services (PCS)/incl. NFIP losses (see “Terms and selection criteria” on page 42).

21 Dead and missing.


Table 12 The 40 worst catastrophes in terms of victims (1970–2016)

Victims19

Insured loss20

(USD mn, indexed to 2016) Start date Event Country/region

300 000 – 11.11.1970 Storm and flood catastrophe Bangladesh255 000 – 28.07.1976 Earthquake (Mw 7.6) China222 570 110 12.01.2010 Earthquake (Mw 7.0), aftershocks Haiti220 000 2541 26.12.2004 Earthquake (Mw 9) triggers tsunami in Indian Ocean Indonesia, Thailand, et. al.138 373 – 02.05.2008 Tropical cyclone Nargis, Irrawaddy Delta flooded Myanmar, Bay of Bengal138 000 4 29.04.1991 Tropical cyclone Gorky Bangladesh

87 449 409 12.05.2008 Earthquake (Mw 7.9) in Sichuan China74 310 – 08.10.2005 Earthquake (Mw 7.6); aftershocks, landslides Pakistan, India, Afghanistan66 000 – 31.05.1970 Earthquake (Mw 7.9) triggers rock slide and floods Peru55 630 – 15.06.2010 Heat wave, temperatures of up to 40°C Russia, Czech Republic40 000 211 20.06.1990 Earthquake (Mw 7.4), landslides Iran35 000 1645 01.06.2003 Heat wave and drought in Europe France, Italy, Germany, et. al.26 271 – 26.12.2003 Earthquake (Mw 6.5) destroys 85% of Bam Iran25 000 – 07.12.1988 Earthquake (Mw 6.8) Armenia25 000 – 16.09.1978 Earthquake (Mw 7.7) in Tabas Iran23 086 – 13.11.1985 Volcanic eruption on Nevado del Ruiz triggers lahars Colombia22 300 316 04.02.1976 Earthquake (Mw 7.5) Guatemala19 737 136 26.01.2001 Earthquake (Mw 7.6) in Gujarat India, Pakistan19 118 1441 17.08.1999 Earthquake (Mw 7.6) in Izmit Turkey18 451 37 344 11.03.2011 Earthquake (Mw 9.0) triggers tsunami Japan15 000 144 29.10.1999 Tropical cyclone 05B in Orissa India14 204 – 20.11.1977 Tropical cyclone in Andhra Pradesh India11 683 589 22.10.1998 Hurricane Mitch in Central America Honduras, Nicaragua, et. al.11 069 – 25.05.1985 Tropical cyclone in Bay of Bengal Bangladesh10 800 – 26.10.1971 Odisha cyclone, flooding in Bay of Bengal India10 000 317 12.12.1999 Floods, mudflows and landslides Venezuela

9500 1056 19.09.1985 Earthquake (Mw 8.0) Mexico9475 0.4 30.09.1993 Earthquake (Mw 6.4) India8960 162 25.04.2015 Earthquake Mw 7.8 Nepal, India, China, Bangladesh8135 525 08.11.2013 Typhoon Haiyan, storm surge Philippines, Vietnam, China, Palau7079 – 17.08.1976 Earthquake (Mw 7.1) triggers tsunami in Moro Gulf Philippines6434 3893 17.01.1995 Great Hanshin earthquake (Mw 6.9) in Kobe Japan6304 – 05.11.1991 Typhoon Thelma (Uring) Philippines6000 – 02.12.1984 Accident in chemical plant – methyl isocyanates released India6000 – 01.06.1976 Heat wave, drought France5749 48 27.05.2006 Earthquake (Mw 6.4); Bantul destroyed Indonesia5748 515 14.06.2013 Floods caused by heavy monsoon rains India5422 – 25.06.1976 Earthquake (Mw 7.1) Indonesia5374 – 10.04.1972 Earthquake (Mw 6.6) in Fars Iran5300 – 28.12.1974 Earthquake (Mw 6.0) Pakistan

Note: Mw = moment magnitude scale.


22 Dead and missing23 Property and business interruption, excluding liability and life insurance losses.


Terms and selection criteria

Natural catastrophesThe term “natural catastrophe” refers to an event caused by natural forces. Such an event generally results in a large number of individual losses involving many insurance policies. The scale of the losses resulting from a catastrophe depends not only on the severity of the natural forces concerned, but also on man-made factors, such as building design or the efficiency of disaster control in the afflicted region. In this sigma study, natural catastrophes are subdivided into the following categories: floods, storms, earthquakes, droughts/forest fires/heat waves, cold waves/frost, hail, tsunamis, and other natural catastrophes.

Man-made disastersThis study categorises major events associated with human activities as “man-made” or “technical” disasters. Generally, a large object in a very limited space is affected, which is covered by a small number of insurance policies. War, civil war, and war-like events are excluded. sigma subdivides man-made disasters into the following categories: major fires and explosions, aviation and space disasters, shipping disasters, rail disasters, mining accidents, collapse of buildings/bridges, and miscellaneous (including terrorism). In Tables 9 and 10 (pages 23–39), all major natural catastrophes and man-made disasters and the associated losses are listed chronologically.

Economic lossesFor the purposes of the present sigma study, economic losses are all the financial losses directly attributable to a major event, ie damage to buildings, infrastructure, vehicles etc. The term also includes losses due to business interruption as a direct consequence of the property damage. Insured losses are gross of any reinsurance, be it provided by commercial or government schemes. A figure identified as “total damage” or “economic loss” includes all damage, insured and uninsured. Total loss figures do not include indirect financial losses – ie loss of earnings by suppliers due to disabled businesses, estimated shortfalls in GDP and non-economic losses, such as loss of reputation or impaired quality of life.

Generally, total (or economic) losses are estimated and communicated in very different ways. As a result, they are not directly comparable and should be seen only as an indication of the general order of magnitude.

Insured losses”Losses” refer to all insured losses except liability. Leaving aside liability losses, on one hand, allows a relatively swift assessment of the insurance year; on the other hand, however, it tends to understate the cost of man-made disasters. Life insurance losses are also not included.

NFIP flood damage in the USThe sigma catastrophe database also includes flood damage covered by the National Flood Insurance Program (NFIP) in the US, provided that it fulfils the sigma selection criteria.

A natural catastrophe is caused by natural forces.

A man-made or technical disaster is triggered by human activities.

Losses due to property damage and business interruption that are directly attributable to major events are included in this study.

The amount of the economic losses is a general indication only.

The term “losses” refer to insured losses, but do not include liability.

NFIP flood damage in the US is included.


Selection criteriasigma has been publishing tables listing major losses since 1970. Thresholds with respect to casualties – the number of dead, missing, severely injured, and homeless – also make it possible to tabulate events in regions where the insurance penetration is below average.

For the 2016 reporting year, the lower loss thresholds were set as follows:

Insured losses (claims):Maritime disasters USD 19.9 millionAviation USD 39.8 millionOther losses USD 49.5 million

or Total losses: USD 99.0 million

or Casualties:Dead or missing 20Injured 50Homeless 2 000


Adjustment for inflation, changes to published data, informationsigma converts all losses for the occurrence year not given in USD into USD using the end-of-year exchange rate. To adjust for inflation, these USD values are extrapolated using the US consumer price index to give current (2016) values.

This can be illustrated by examining the insured property losses arising from the floods which occurred in the UK between 29 October abd 10 November 2000:

Insured loss at 2000 prices: USD 1 045.7 million Insured loss at 2016 prices: USD 1 457.5 million

Alternatively, were one to adjust the losses in the original currency (GBP) for inflation and then convert them to USD using the current exchange rate, one would end up with an insured loss at 2016 prices of USD 1 192.5 million, 18% less than with the standard sigma method. The reason for the difference is that the value of the GBP declined by almost 18% against the USD in the period 2000–2016. The difference in inflation between the US (39.4%) and the UK (38.5%) over the same period was slightly less than 1%.

Floods UK Exchange rate US inflation

29 October–10 November 2000 GBPmn USD/GBP USDmn USDmnOriginal loss 700.0 1.494 1045.7 1045.7

Level of consumer price index 2000 72.7 100.0Level of consumer price index 2016 100.7 139.4Inflation factor 1.385 1.394

Adjusted for inflation to 2016 969.3 1.230 1192.5 1457.5Comparison 82% 100%

Source: Swiss Re Institute.

Thresholds for insured losses and casualties in 2016

Losses are determined using year-end exchange rates and are then adjusted for inflation.

Figure 9 Alternative methods of adjusting for inflation, by comparison


Terms and selection criteria

If changes to the loss amounts of previously published events become known, sigma takes these into account in its database. However, these changes only become evident when an event appears in the table of the 40 most costly insured losses or the 40 disasters with the most fatalities since 1970 (See Tables 11 and 12 on pages 40–41).

In the chronological lists of all man-made disasters, the insured losses are not shown for data protection reasons. However, the total of these insured losses is included in the list of major losses in 2016 according to loss category. sigma does not provide further information on individual insured losses or about updates made to published data.

SourcesInformation is collected from newspapers, direct insurance and reinsurance periodicals, specialist publications (in printed or electronic form) and reports from insurers and reinsurers.24 In no event shall Swiss Re be liable for any loss or damage arising in connection with the use of this information (see the copyright information on backpage).

Exchange rate used, 25 national currency per USD

Country Currency Exchange rate, end 2016

United Arab Emirates AED 3.6724Australia AUD 1.3808Canada CAD 1.3408Europe CHF 1.0162China CNY 6.9444Costa Rica CRC 555.5556Egypt EGP 18.1488Euro area EUR 0.9481Fiji FJD 2.1142UK GBP 0.8089India INR 68.0272Japan JPY 116.2791South Korea KRW 1250Sri Lanka LKR 149.2537New Zealand NZD 1.4339Oman OMR 0.3850Philippines PHP 49.5050Qatar QAR 3.6417Russia RUB 60.9756Tonga TOP 2.3095Taiwan TWD 32.3625US USD 1.0000South Africa ZAR 13.6799

Source: Swiss Re Institute.

24 Natural catastrophes in the US: those sigma figures which are based on estimates of Property Claim Services (PCS), a unit of the Insurance Services Office, Inc (ISO), are given for each individual event in ranges defined by PCS. The estimates are the property of ISO and may not be printed or used for any purpose, including use as a component in any financial instruments, without the express consent of ISO.

25 The losses for 2016 were converted to USD using these exchange rates. No losses in any other currencies were reported

Changes to loss amounts of previously published events are updated in the sigma database.

Only public information used for man-made disasters

Newspapers, direct insurance and reinsurance periodicals, specialist publications and other reports are used to compile this study.


Recent sigma publications

2017 No 1 Cyber: getting to grips with a complex risk No 2 Natural catastrophes and man-made disasters in 2016: a year of widespread damages

2016 No 1 Natural catastrophes and man-made disasters in 2015: Asia suffers substantial losses No 2 Insuring the frontier markets No 3 World insurance 2015: steady growth amid regional disparities No 4 Mutual insurance in the 21st century: back to the future? No 5 Strategic reinsurance and insurance: the increasing trend of customised solutions

2015 No 1 Keeping healthy in emerging markets: insurance can help No 2 Natural catastrophes and man-made disasters in 2014: convective and winter storms generate most losses No 3 M & A in insurance: start of a new wave? No 4 World insurance in 2014: back to life No 5 Underinsurance of property risks: closing the gap No 6 Life insurance in the digital age: fundamental transformation ahead

2014 No 1 Natural catastrophes and man-made disasters in 2013: large losses from floods and hail; Haiyan hits the Philippines

No 2 Digital distribution in insurance: a quiet revolution No 3 World insurance in 2013: steering towards recovery No 4 Liability claims trends: emerging risks and rebounding economic drivers No 5 How will we care? Finding sustainable long-term care solutions for an ageing world

2013 No 1 Partnering for food security in emerging markets No 2 Natural catastrophes and man-made disasters in 2012: A year of extreme weather events in the US No 3 World insurance 2012: Progressing on the long and winding road to recovery No 4 Navigating recent developments in marine and airline insurance No 5 Urbanisation in emerging markets: boon and bane for insurers No 6 Life insurance: focusing on the consumer

2012 No 1 Understanding profitability in life insurance No 2 Natural catastrophes and man-made disasters in 2011: historic losses surface from record earthquakes and floods No 3 World insurance in 2011: non-life ready for take-off No 4 Facing the interest rate challenge No 5 Insuring ever-evolving commercial risks No 6 Insurance accounting reform: a glass half empty or half full?

2011 No 1 Natural catastrophes and man-made disasters in 2010: a year of devastating and costly events No 2 World insurance in 2010 No 3 State involvement in insurance markets No 4 Product innovation in non-life insurance markets: where little “i” meets big “I” No 5 Insurance in emerging markets: growth drivers and profitability

2010 No 1 Natural catastrophes and man-made disasters in 2009: catastrophes claim fewer victims, insured losses fall

No 2 World insurance in 2009: premiums dipped, but industry capital improved No 3 Regulatory issues in insurance No 4 The impact of inflation on insurers No 5 Insurance investment in a challenging global environment No 6 Microinsurance — risk protection for 4 billion people

2009 No 1 Scenario analysis in insurance No 2 Natural catastrophes and man-made disasters in 2008:

North America and Asia suffer heavy losses No 3 World insurance in 2008: life premiums fall in the industrialised countries —

strong growth in the emerging economies

Published by Swiss Re InstituteP.O. Box 8022 ZurichSwitzerland

Telephone +41 43 285 2551Fax +41 43 282 0075E-Mail: [email protected]

Armonk Office175 King StreetArmonk, NY 10504

Telephone +1 914 828 8000

Hong Kong Office18 Harbour Road, WanchaiCentral Plaza, 61st FloorHong Kong, SAR

Telephone + 852 25 82 5644

AuthorsLucia BevereTelephone +41 43 285 9279

Rajeev SharanTelephone +91 80 4900 2172

Sarah BarrettTelephone +1 914 828 8491

Caspar Honegger Telephone +41 43 285 6014

sigma editorPaul RonkeTelephone +41 43 285 2660

Editor in chiefKurt Karl,Chief Economist of Swiss Re is responsible for the sigma series.

Explore and visualise sigma data on natural catastrophes and the world insurance markets at www.sigma-explorer.com

© 2017 Swiss Re. All rights reserved.

The editorial deadline for this study was 10 February 2017.

sigma is available in English (original language), German, French, Spanish, Chinese and Japanese.

sigma is available on Swiss Re’s website: www.swissre.com/sigma

The internet version may contain slightly updated information.

Translations:German: Diction AGFrench: ithaxa Communications SARLSpanish: Traductores Asociados Valencia S.L.

Graphic design and production:Corporate Real Estate & Services / Media Production, Zurich

Printing: Multicolor Print AG, Baar

This report is printed on sustainably produced paper. The wood used comes from forest certified to 100% by the Forest Stewardship Council (FSC).

The entire content of this sigma edition is subject to copyright with all rights reserved. The information may be used for private or internal purposes, provided that any copyright or other proprietary notices are not removed. Electronic reuse of the data published in sigma is prohibited.

Reproduction in whole or in part or use for any public purpose is permitted only with the prior written approval of Swiss Re Institute and if the source reference “Swiss Re, sigma No 2/2017” is indicated. Courtesy copies are appreciated.

Although all the information used in this study was taken from reliable sources, Swiss Re does not accept any responsibility for the accuracy or comprehensiveness of the information given or forward looking statements made. The information provided and forward-looking statements made are for informational purposes only and in no way constitute or should be taken to reflect Swiss Re’s position, in particular in relation to any ongoing or future dispute. In no event shall Swiss Re be liable for any loss or damage arising in connection with the use of this information and readers are cautioned not to place undue reliance on forward-looking statements. Swiss Re undertakes no obligation to publicly revise or update any forward-looking statements, whether as a result of new information, future events or otherwise.

Order no: 270_0217_en

tel:+1%20914%20828%208491

Swiss Re InstituteMythenquai 50 /60 P.O. Box 8022 Zurich Switzerland

Telephone + 41 43 285 2551 Fax +41 43 282 0075 [email protected] www.swissre.com

Documents

Natural catastrophes and man-made disasters in 2016 · 2019-12-12 · Economic losses from natural catastrophes and man-made disasters across the world were an estimated USD 175 billion